LocationLists

Official16 tools

by kylehawke-stack

US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).

MCP Server: io.github.kylehawke-stack/locationlists

The MCP server (io.github.kylehawke-stack/locationlists) provides functions to search, preview, and buy CSV datasets focused on US business locations, including dealer networks, contractors, and chains. Its description centers on dataset discovery and transaction-oriented access to CSV files.

🛠️ Key Features

Search CSV datasets of US business locations
Preview dataset contents
Buy CSV datasets
Coverage includes dealer networks, contractors, and chains

🚀 Use Cases

Locate and evaluate CSV data for US dealer networks
Find contractors and business location datasets in CSV form
Source CSV data for chains operating in the US

⚡ Developer Benefits

Dataset-oriented workflow (search → preview → purchase)
Direct alignment with CSV-based location data needs

⚠️ Limitations

Only described for CSV datasets of US business locations (dealer networks, contractors, chains); other formats or geographies are not specified

Topics

datasetsmcpmcp-servermodel-context-protocolbusiness-locationsagent-paymentsbasebusiness-datalocation-datausdcx402

Captured live from the server via tools/list.

search_datasets

Find LocationLists datasets by brand, kind of business or industry (e.g. 'bobcat', 'restaurants', 'bank branches', 'dental practices', 'hardware stores'). Returns EVERY matching dataset, best first, with slug, name, business type, industry, record count, coverage, whether it can be searched by distance (distanceSearch), and page URL. A kind of business or an industry in the query matches every dataset of that kind, and `kinds` names it as a category that count_locations can combine into one answer. Each brand or chain is its own dataset. After finding one you can filter it by city, state, zip, any column, or a radius around a place (e.g. within 25 miles of Los Angeles, CA) when it has coordinates: use count_locations for how many match, and get_sample with the same filters for that count plus the list's fixed free sample rows, each marked matches_your_question. Both are free. To cover several chains near one place, pass datasets or category and a total to count_locations, and you get one answer, one price and one file.

Parameters3

query	string	optional	Free text: brand, kind of business, industry or product
category	string	optional	Restrict to one catalog category, industry or subcategory
limit	integer	optional	Max results (default: every match)

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Free text: brand, kind of business, industry or product"
    },
    "category": {
      "type": "string",
      "description": "Restrict to one catalog category, industry or subcategory"
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 1000,
      "description": "Max results (default: every match)"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

relate_locations

Free. How one set of places relates to another, by straight-line distance. The base set is the usual dataset / datasets / category plus filters; relate.anchor is the other set, given the same way. Modes: nearest (each base row's k<=3 nearest anchors with miles), count_within (rank base rows by how many anchors are within radius_miles), within_any (base rows with at least one anchor within radius_miles), none_within (base rows with no anchor within radius_miles). Mode next_best (no anchor, needs the base set's state) ranks the candidate NEW sites in that state by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture; every candidate names its matched factors. Returns counts for both sets (rows without coordinates are left out and counted), summary stats, up to 3 preview rows, the price of the full answer (base rows plus the anchor rows named, each at its dataset's per-row rate, one card fee) and how to buy it with query_locations or create_query_checkout using the same arguments. Example: {"dataset": "<slug>", "state": "VA", "relate": {"mode": "nearest", "k": 1, "anchor": {"dataset": "<other slug>"}}}.

Parameters14

dataset	string	optional
datasets	array	optional
category	string	optional
exclude	array	optional	Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded).
state	string	optional
city	string	optional
county	string	optional
zip	string	optional
where	array	optional
near	object	optional
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
relate	object	required	Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate.
total	integer	optional	Base rows wanted, first in answer order

Raw schema

{
  "type": "object",
  "properties": {
    "dataset": {
      "type": "string"
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1
    },
    "category": {
      "type": "string"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."
    },
    "state": {
      "type": "string"
    },
    "city": {
      "type": "string"
    },
    "county": {
      "type": "string"
    },
    "zip": {
      "type": "string"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ]
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string"
        },
        "zip": {
          "type": "string"
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500
        }
      },
      "additionalProperties": false
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "relate": {
      "type": "object",
      "properties": {
        "mode": {
          "type": "string",
          "enum": [
            "nearest",
            "count_within",
            "within_any",
            "none_within",
            "same_place",
            "not_same_place",
            "next_best",
            "overlap"
          ],
          "description": "nearest | count_within | within_any | none_within | same_place | not_same_place | next_best | overlap"
        },
        "anchor": {
          "type": "object",
          "properties": {
            "dataset": {
              "$ref": "#/properties/dataset"
            },
            "datasets": {
              "$ref": "#/properties/datasets"
            },
            "category": {
              "$ref": "#/properties/category"
            },
            "exclude": {
              "$ref": "#/properties/exclude"
            },
            "state": {
              "$ref": "#/properties/state"
            },
            "city": {
              "$ref": "#/properties/city"
            },
            "county": {
              "$ref": "#/properties/county"
            },
            "zip": {
              "$ref": "#/properties/zip"
            },
            "where": {
              "$ref": "#/properties/where"
            },
            "near": {
              "$ref": "#/properties/near"
            },
            "area_where": {
              "$ref": "#/properties/area_where"
            },
            "area_columns": {
              "$ref": "#/properties/area_columns"
            }
          },
          "additionalProperties": false,
          "description": "The other set: dataset, datasets or category, plus filters. Every mode but next_best needs it"
        },
        "lambda": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 10,
          "description": "next_best only: the Huff distance-decay exponent (default 2, the traditional value)"
        },
        "limit": {
          "type": "integer",
          "minimum": 1,
          "maximum": 50,
          "description": "next_best only: candidates ranked (default 50)"
        },
        "anchors": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "dataset": {
                "$ref": "#/properties/dataset"
              },
              "datasets": {
                "$ref": "#/properties/datasets"
              },
              "category": {
                "$ref": "#/properties/category"
              },
              "exclude": {
                "$ref": "#/properties/exclude"
              },
              "state": {
                "$ref": "#/properties/state"
              },
              "city": {
                "$ref": "#/properties/city"
              },
              "county": {
                "$ref": "#/properties/county"
              },
              "zip": {
                "$ref": "#/properties/zip"
              },
              "where": {
                "$ref": "#/properties/where"
              },
              "near": {
                "$ref": "#/properties/near"
              },
              "area_where": {
                "$ref": "#/properties/area_where"
              },
              "area_columns": {
                "$ref": "#/properties/area_columns"
              }
            },
            "additionalProperties": false
          },
          "maxItems": 2,
          "description": "same_place only: further sets (c, d) after anchor (b), each given the same way"
        },
        "also": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "same_place only: the sets a base row must be the same place as some row of, by label (\"b\", \"c\"); default every set"
        },
        "not_also": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "same_place only: the sets a base row must NOT be the same place as any row of; not_same_place with one set is [\"b\"]"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 250
        },
        "within_drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Drive time instead of radius_miles (5-60 minutes). count_within / within_any / none_within only; at most 60 base rows."
        },
        "k": {
          "type": "integer",
          "minimum": 1,
          "maximum": 3,
          "description": "nearest: how many anchors per base row. next_best: H3 rings counted as nearby, 1-3 (default 2, about a mile)"
        },
        "order": {
          "type": "string",
          "enum": [
            "asc",
            "desc"
          ]
        },
        "reveal_anchor": {
          "type": "boolean"
        },
        "anchor_all": {
          "type": "array",
          "items": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "minItems": 2,
          "maxItems": 3,
          "description": "count_within / within_any: ONE OF EACH instead of one of any. The anchor set's dataset slugs grouped as the question named them — [[\"a\"],[\"b\"]] keeps only base rows with a row of BOTH within the distance. Omit for the ordinary any-of reading."
        }
      },
      "required": [
        "mode"
      ],
      "additionalProperties": false,
      "description": "Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate."
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "Base rows wanted, first in answer order"
    }
  },
  "required": [
    "relate"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

count_by_area

Free, counts only. Counts places per county / zip / state / metro for 1 to 4 labeled sets (each a dataset, datasets or category plus filters), and compares them: has (areas with at least one of every listed set) and lacks (areas with none of any listed set). E.g. counties that have set a but no set b; ZIPs where a closure-filtered set exists and another set still has places. Rows that cannot be placed are counted, never read as zero. Example: {"by": "county", "sets": [{"label": "a", "dataset": "<slug>"}, {"label": "b", "dataset": "<other slug>"}], "has": ["a"], "lacks": ["b"], "in_state": "VA"}.

Parameters10

by	string	required
sets	array	required
has	array	optional
lacks	array	optional
in_state	string	optional	Only areas in this state
area_where	array	optional	Only areas meeting a Census condition, each as "<kind>:<attribute><op><value>" about the same kind as by (or a state), e.g. "county:population>500000" with lacks for "counties over 500k people with no X". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
per	number	optional	Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field.
per_field	string	optional	With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, and every <sector>_establishments / <sector>_employees.
limit	integer	optional	Areas listed (default: every matching area when 500 or fewer match, else the first 100)
all	boolean	optional	List every matching area, however many

Raw schema

{
  "type": "object",
  "properties": {
    "by": {
      "type": "string",
      "enum": [
        "county",
        "zip",
        "state",
        "metro"
      ]
    },
    "sets": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "label": {
            "type": "string",
            "description": "Short name, e.g. \"a\" or \"closed\""
          },
          "dataset": {
            "type": "string"
          },
          "datasets": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "minItems": 1
          },
          "category": {
            "type": "string"
          },
          "exclude": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."
          },
          "state": {
            "type": "string"
          },
          "city": {
            "type": "string"
          },
          "county": {
            "type": "string"
          },
          "zip": {
            "type": "string"
          },
          "where": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "field": {
                  "type": "string"
                },
                "op": {
                  "type": "string",
                  "enum": [
                    "eq",
                    "ne",
                    "gt",
                    "gte",
                    "lt",
                    "lte",
                    "in",
                    "contains",
                    "starts_with",
                    "is_blank",
                    "not_blank"
                  ]
                },
                "value": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "array",
                      "items": {
                        "type": [
                          "string",
                          "number"
                        ]
                      }
                    }
                  ]
                }
              },
              "required": [
                "field",
                "op"
              ],
              "additionalProperties": false
            },
            "maxItems": 12
          },
          "near": {
            "type": "object",
            "properties": {
              "place": {
                "type": "string"
              },
              "zip": {
                "type": "string"
              },
              "lat": {
                "type": "number"
              },
              "lng": {
                "type": "number"
              },
              "radius_miles": {
                "type": "number",
                "exclusiveMinimum": 0,
                "maximum": 500
              }
            },
            "additionalProperties": false
          },
          "area_where": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "maxItems": 6,
            "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
          },
          "area_columns": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "maxItems": 6,
            "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
          }
        },
        "required": [
          "label"
        ],
        "additionalProperties": false
      },
      "minItems": 1,
      "maxItems": 4
    },
    "has": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "lacks": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "in_state": {
      "type": "string",
      "description": "Only areas in this state"
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "per": {
      "type": "number",
      "minimum": 1,
      "description": "Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field."
    },
    "per_field": {
      "type": "string",
      "description": "With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, and every <sector>_establishments / <sector>_employees."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 1000,
      "description": "Areas listed (default: every matching area when 500 or fewer match, else the first 100)"
    },
    "all": {
      "type": "boolean",
      "description": "List every matching area, however many"
    }
  },
  "required": [
    "by",
    "sets"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

cotenancy

Free, counts only. How two sets of places sit together: the share of set a within radius_miles of set b and of b within radius_miles of a, how many places overlap, and the county / zip / state / metro areas that have both, only a, or only b (top 10 of each named). Each set is a dataset, datasets or category plus filters, the same as count_locations; any US brand or kind of place works, including Overture lists from search_datasets. Example: {"a": {"dataset": "<slug>"}, "b": {"dataset": "<other slug>"}, "radius_miles": 1, "by": "county"}.

Parameters5

a	object	required	First set: dataset, datasets or category, plus filters
b	object	required	Second set, given the same way
radius_miles	number	optional	Distance that counts as together (default 1 mile)
by	string	optional	Area unit for both/only-a/only-b (default county)
in_state	string	optional	Only areas in this state

Raw schema

{
  "type": "object",
  "properties": {
    "a": {
      "type": "object",
      "properties": {
        "dataset": {
          "type": "string"
        },
        "datasets": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "minItems": 1
        },
        "category": {
          "type": "string"
        },
        "exclude": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded)."
        },
        "state": {
          "type": "string"
        },
        "city": {
          "type": "string"
        },
        "county": {
          "type": "string"
        },
        "zip": {
          "type": "string"
        },
        "where": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "field": {
                "type": "string"
              },
              "op": {
                "type": "string",
                "enum": [
                  "eq",
                  "ne",
                  "gt",
                  "gte",
                  "lt",
                  "lte",
                  "in",
                  "contains",
                  "starts_with",
                  "is_blank",
                  "not_blank"
                ]
              },
              "value": {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "array",
                    "items": {
                      "type": [
                        "string",
                        "number"
                      ]
                    }
                  }
                ]
              }
            },
            "required": [
              "field",
              "op"
            ],
            "additionalProperties": false
          },
          "maxItems": 12
        },
        "near": {
          "type": "object",
          "properties": {
            "place": {
              "type": "string"
            },
            "zip": {
              "type": "string"
            },
            "lat": {
              "type": "number"
            },
            "lng": {
              "type": "number"
            },
            "radius_miles": {
              "type": "number",
              "exclusiveMinimum": 0,
              "maximum": 500
            }
          },
          "additionalProperties": false
        },
        "area_where": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "maxItems": 6,
          "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
        },
        "area_columns": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "maxItems": 6,
          "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
        }
      },
      "additionalProperties": false,
      "description": "First set: dataset, datasets or category, plus filters"
    },
    "b": {
      "type": "object",
      "properties": {
        "dataset": {
          "$ref": "#/properties/a/properties/dataset"
        },
        "datasets": {
          "$ref": "#/properties/a/properties/datasets"
        },
        "category": {
          "$ref": "#/properties/a/properties/category"
        },
        "exclude": {
          "$ref": "#/properties/a/properties/exclude"
        },
        "state": {
          "$ref": "#/properties/a/properties/state"
        },
        "city": {
          "$ref": "#/properties/a/properties/city"
        },
        "county": {
          "$ref": "#/properties/a/properties/county"
        },
        "zip": {
          "$ref": "#/properties/a/properties/zip"
        },
        "where": {
          "$ref": "#/properties/a/properties/where"
        },
        "near": {
          "$ref": "#/properties/a/properties/near"
        },
        "area_where": {
          "$ref": "#/properties/a/properties/area_where"
        },
        "area_columns": {
          "$ref": "#/properties/a/properties/area_columns"
        }
      },
      "additionalProperties": false,
      "description": "Second set, given the same way"
    },
    "radius_miles": {
      "type": "number",
      "exclusiveMinimum": 0,
      "maximum": 250,
      "description": "Distance that counts as together (default 1 mile)"
    },
    "by": {
      "type": "string",
      "enum": [
        "county",
        "zip",
        "state",
        "metro"
      ],
      "description": "Area unit for both/only-a/only-b (default county)"
    },
    "in_state": {
      "type": "string",
      "description": "Only areas in this state"
    }
  },
  "required": [
    "a",
    "b"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_dataset

Full record for one dataset: fields with descriptions, record and state counts, coverage, whether it can be searched by distance, advertised refresh cadence AND the real last-modified date of the file, FAQs, sample URL and the dataset's page on locationlists.com.

Parameters1

slug

string

required

Dataset slug from search_datasets, e.g. bobcat-dealers

Raw schema

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Dataset slug from search_datasets, e.g. bobcat-dealers"
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

count_locations

Free. How many rows of one dataset match a filter — on geography AND any other column (e.g. nonprofits with revenue_amt gt 2000000, dealers with dealerClass eq 'Elite'). Also reports how many rows were excluded only because a tested column was blank, so a thin column is not mistaken for a small answer; a small or empty answer says how many rows each condition removed and what the column really holds. Returns the exact card price of the matching rows, a link where the user can see and buy them, and the same rows in a cheaper list when one has them. Works for geography: city, state, county, zip, or `near` a place ("Los Angeles, CA"), zip or lat/lng within radius_miles or drive_minutes, on lists with coordinates. get_sample takes the same filters and returns the count plus the list's fixed free sample rows, marked matches_your_question. The result's `next` says exactly how to get every matching row. To cover several chains near one place, pass datasets or category (e.g. "retail" or "restaurant") and a total instead of dataset: one answer with counts per dataset, duplicates removed and up to 3 preview rows, one price and one file. Use get_dataset first for the column names. Scans the live file, so it can take several seconds on large datasets.

Parameters13

dataset	string	optional	Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.
state	string	optional	Shortcut for where state eq <value>. Two-letter code.
city	string	optional	Shortcut for where city eq <value>
county	string	optional	Shortcut for where county eq <value>
zip	string	optional	Shortcut for where zip eq <value>
where	array	optional	Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.
near	object	optional	Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted.
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
datasets	array	optional	Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)
category	string	optional	Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)
exclude	array	optional	With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)
total	integer	optional	With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches

Raw schema

{
  "type": "object",
  "properties": {
    "dataset": {
      "type": "string",
      "description": "Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead."
    },
    "state": {
      "type": "string",
      "description": "Shortcut for where state eq <value>. Two-letter code."
    },
    "city": {
      "type": "string",
      "description": "Shortcut for where city eq <value>"
    },
    "county": {
      "type": "string",
      "description": "Shortcut for where county eq <value>"
    },
    "zip": {
      "type": "string",
      "description": "Shortcut for where zip eq <value>"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string",
            "description": "Column name as listed by get_dataset (columns[].name), e.g. revenue_amt"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ],
            "description": "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12,
      "description": "Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds."
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string",
          "description": "City or town with state, e.g. \"Topeka, KS\". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which."
        },
        "zip": {
          "type": "string",
          "description": "5-digit zip, e.g. \"66603\""
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500,
          "description": "Only rows within this straight-line distance"
        },
        "drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"
        }
      },
      "additionalProperties": false,
      "description": "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 150,
      "description": "Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)"
    },
    "category": {
      "type": "string",
      "description": "Instead of dataset: every dataset of one kind — \"retail\" (store chains), an industry or subcategory, or a kind of business such as \"restaurant\" or \"bank branch\" (search_datasets names these)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_sample

Free. Real rows from the live file, as JSON plus CSV text. Show these to the user so they can judge the fields and quality. Without filters: up to 10 rows spread across the whole dataset. With filters (the same ones count_locations takes: city, state, county, zip, `where` on any column, or `near` a place such as {place: "Los Angeles, CA", radius_miles: 25} or {place: "Richmond, VA", drive_minutes: 30} on lists with coordinates): how many rows match, plus up to 3To preview several chains at once, pass datasets or category (e.g. "retail" or "restaurant") instead of slug: one combined answer with counts per dataset, duplicates removed and up to 3 rows across them.

Parameters14

slug	string	optional	Dataset slug. Or datasets / category to preview several at once.
rows	integer	optional	Rows to return (default 10; with filters at most 3)
state	string	optional	Shortcut for where state eq <value>. Two-letter code.
city	string	optional	Shortcut for where city eq <value>
county	string	optional	Shortcut for where county eq <value>
zip	string	optional	Shortcut for where zip eq <value>
where	array	optional	Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.
near	object	optional	Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted.
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
datasets	array	optional	Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)
category	string	optional	Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)
exclude	array	optional	With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)
total	integer	optional	With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches

Raw schema

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Dataset slug. Or datasets / category to preview several at once."
    },
    "rows": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10,
      "description": "Rows to return (default 10; with filters at most 3)"
    },
    "state": {
      "type": "string",
      "description": "Shortcut for where state eq <value>. Two-letter code."
    },
    "city": {
      "type": "string",
      "description": "Shortcut for where city eq <value>"
    },
    "county": {
      "type": "string",
      "description": "Shortcut for where county eq <value>"
    },
    "zip": {
      "type": "string",
      "description": "Shortcut for where zip eq <value>"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string",
            "description": "Column name as listed by get_dataset (columns[].name), e.g. revenue_amt"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ],
            "description": "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12,
      "description": "Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds."
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string",
          "description": "City or town with state, e.g. \"Topeka, KS\". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which."
        },
        "zip": {
          "type": "string",
          "description": "5-digit zip, e.g. \"66603\""
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500,
          "description": "Only rows within this straight-line distance"
        },
        "drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"
        }
      },
      "additionalProperties": false,
      "description": "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 150,
      "description": "Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)"
    },
    "category": {
      "type": "string",
      "description": "Instead of dataset: every dataset of one kind — \"retail\" (store chains), an industry or subcategory, or a kind of business such as \"restaurant\" or \"bank branch\" (search_datasets names these)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

request_list

Ask LocationLists to add a list we do not have yet. Use it when search_datasets finds nothing that fits, or the user wants a brand, place or kind of business we do not publish. BEFORE calling: ask the user whether to send the request, and ask for their email so we can tell them when the list is ready. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Called with neither email nor email_declined, it sends nothing and asks for the email. The request goes to the LocationLists team, the same place as the request box on locationlists.com. We add new datasets every day and prioritize requested ones; there is no promised date. Free, nothing is charged.

Parameters7

topic	string	required	The list the user wants, in their words (2-200 characters)
email	string	optional	The user's email, only if they gave it, so we can tell them when the list is ready
email_declined	boolean	optional	True only when you asked the user for their email and they chose not to leave one
use_case	string	optional	What they will use the list for, if they said (e.g. sales outreach, territory planning)
details	string	optional	Anything else they specified: places, fields needed (phone, website, email), timing
name	string	optional	The user's name, if they gave it
company	string	optional	The user's company, if they gave it

Raw schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "The list the user wants, in their words (2-200 characters)"
    },
    "email": {
      "type": "string",
      "description": "The user's email, only if they gave it, so we can tell them when the list is ready"
    },
    "email_declined": {
      "type": "boolean",
      "description": "True only when you asked the user for their email and they chose not to leave one"
    },
    "use_case": {
      "type": "string",
      "description": "What they will use the list for, if they said (e.g. sales outreach, territory planning)"
    },
    "details": {
      "type": "string",
      "description": "Anything else they specified: places, fields needed (phone, website, email), timing"
    },
    "name": {
      "type": "string",
      "description": "The user's name, if they gave it"
    },
    "company": {
      "type": "string",
      "description": "The user's company, if they gave it"
    }
  },
  "required": [
    "topic"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

send_feedback

Send a message to the LocationLists team: wrong or missing data in a dataset, something that did not work, a pricing question, an idea, or anything else. Ask the user before sending and use their words. Ask for their email so the team can reply, and pass it only if they gave it; never guess or invent one. Called with neither email nor email_declined, it sends nothing and asks for the email. Free.

Parameters5

message	string	required	The feedback, in the user's words (5-4000 characters)
email	string	optional	The user's email, only if they gave it
email_declined	boolean	optional	True only when you asked the user for their email and they chose not to leave one
category	string	optional	What it is about (default other)
dataset	string	optional	Dataset slug it concerns, if any, e.g. generac-dealers

Raw schema

{
  "type": "object",
  "properties": {
    "message": {
      "type": "string",
      "description": "The feedback, in the user's words (5-4000 characters)"
    },
    "email": {
      "type": "string",
      "description": "The user's email, only if they gave it"
    },
    "email_declined": {
      "type": "boolean",
      "description": "True only when you asked the user for their email and they chose not to leave one"
    },
    "category": {
      "type": "string",
      "enum": [
        "bug",
        "data quality",
        "pricing",
        "feature",
        "other"
      ],
      "description": "What it is about (default other)"
    },
    "dataset": {
      "type": "string",
      "description": "Dataset slug it concerns, if any, e.g. generac-dealers"
    }
  },
  "required": [
    "message"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

email_quote

Free. Emails the user a plain-English quote for exactly this request: how many rows match, the card price, a few of the matches and a card checkout link, so they can pay later, from any device, or forward it to whoever holds the card. Takes the same arguments as count_locations: dataset, or datasets / category with total, plus filters. BEFORE calling: ask the user for their email and whether to send it. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Nothing is charged and nothing is bought; the price is checked again when they open the link.

Parameters14

email	string	required	The user's email, as they gave it
dataset	string	optional	Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.
state	string	optional	Shortcut for where state eq <value>. Two-letter code.
city	string	optional	Shortcut for where city eq <value>
county	string	optional	Shortcut for where county eq <value>
zip	string	optional	Shortcut for where zip eq <value>
where	array	optional	Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.
near	object	optional	Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted.
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
datasets	array	optional	Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)
category	string	optional	Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)
exclude	array	optional	With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)
total	integer	optional	With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches

Raw schema

{
  "type": "object",
  "properties": {
    "email": {
      "type": "string",
      "description": "The user's email, as they gave it"
    },
    "dataset": {
      "type": "string",
      "description": "Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead."
    },
    "state": {
      "type": "string",
      "description": "Shortcut for where state eq <value>. Two-letter code."
    },
    "city": {
      "type": "string",
      "description": "Shortcut for where city eq <value>"
    },
    "county": {
      "type": "string",
      "description": "Shortcut for where county eq <value>"
    },
    "zip": {
      "type": "string",
      "description": "Shortcut for where zip eq <value>"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string",
            "description": "Column name as listed by get_dataset (columns[].name), e.g. revenue_amt"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ],
            "description": "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12,
      "description": "Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds."
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string",
          "description": "City or town with state, e.g. \"Topeka, KS\". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which."
        },
        "zip": {
          "type": "string",
          "description": "5-digit zip, e.g. \"66603\""
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500,
          "description": "Only rows within this straight-line distance"
        },
        "drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"
        }
      },
      "additionalProperties": false,
      "description": "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 150,
      "description": "Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)"
    },
    "category": {
      "type": "string",
      "description": "Instead of dataset: every dataset of one kind — \"retail\" (store chains), an industry or subcategory, or a kind of business such as \"restaurant\" or \"bank branch\" (search_datasets names these)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"
    }
  },
  "required": [
    "email"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_quote

Line-item prices and total for a list of dataset slugs. If a bundle covers several requested brands for less, it says so.

Parameters1

slugs

array

required

Dataset slugs

Raw schema

{
  "type": "object",
  "properties": {
    "slugs": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 50,
      "description": "Dataset slugs"
    }
  },
  "required": [
    "slugs"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

create_checkout

Opens a Stripe Checkout session for one dataset and returns the payment URL plus the session id. Give the URL to the user to pay (card, Apple Pay, Google Pay). After payment Stripe emails them a permanent download link; use check_order with the session id to confirm and fetch it. Does not charge anything by itself.

Parameters2

slug	string	required	Dataset slug
email	string	optional	Buyer email, if known — prefills Checkout and is where the download link is sent

Raw schema

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Dataset slug"
    },
    "email": {
      "type": "string",
      "format": "email",
      "description": "Buyer email, if known — prefills Checkout and is where the download link is sent"
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

check_order

Given a Stripe Checkout session id (cs_…), reports whether it is paid and, if so, returns the permanent download link for the CSV. Works for whole files, filtered rows and combined (several-dataset) orders.

Parameters1

sessionId

string

required

Checkout session id from create_checkout

Raw schema

{
  "type": "object",
  "properties": {
    "sessionId": {
      "type": "string",
      "pattern": "^cs_",
      "description": "Checkout session id from create_checkout"
    }
  },
  "required": [
    "sessionId"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

create_query_checkout

For buyers paying by card (no wallet needed): opens a Stripe Checkout for just the rows of one dataset that match a filter, and returns the payment URL to give the user. Takes the same filters as count_locations (state/city/county/zip, `where` on any column, `near`, `order_by`) and up to 10,000 rows. It counts the matches first, so the buyer pays only for rows that exist: the data price is the same per-row price query_locations charges, plus a card processing fee (2.9% + $0.30) added on top and shown separately. After payment the buyer is emailed a CSV download link; check_order with the session id returns it too. No match, a bad column, a distance search on a list without coordinates, or a subset that would cost more than the whole file returns an explanation and creates no checkout — nothing is charged. Agents with a USDC wallet should call query_locations instead. To cover several chains near one place, pass datasets or category and a total instead of dataset: one answer, one price and one checkout for one CSV (source_dataset names each row's dataset, duplicates removed).

Parameters17

dataset	string	optional	Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.
state	string	optional	Shortcut for where state eq <value>. Two-letter code.
city	string	optional	Shortcut for where city eq <value>
county	string	optional	Shortcut for where county eq <value>
zip	string	optional	Shortcut for where zip eq <value>
where	array	optional	Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.
near	object	optional	Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted.
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
datasets	array	optional	Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)
category	string	optional	Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)
exclude	array	optional	With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)
total	integer	optional	With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches
relate	object	optional
order_by	object	optional	Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last.
limit	integer	optional	Most rows to buy (max 10,000). Default: every match up to the max. Priced on the rows that actually match, never more.
email	string	optional	Buyer email, if known — prefills Checkout and is where the download link is sent

Raw schema

{
  "type": "object",
  "properties": {
    "dataset": {
      "type": "string",
      "description": "Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead."
    },
    "state": {
      "type": "string",
      "description": "Shortcut for where state eq <value>. Two-letter code."
    },
    "city": {
      "type": "string",
      "description": "Shortcut for where city eq <value>"
    },
    "county": {
      "type": "string",
      "description": "Shortcut for where county eq <value>"
    },
    "zip": {
      "type": "string",
      "description": "Shortcut for where zip eq <value>"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string",
            "description": "Column name as listed by get_dataset (columns[].name), e.g. revenue_amt"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ],
            "description": "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12,
      "description": "Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds."
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string",
          "description": "City or town with state, e.g. \"Topeka, KS\". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which."
        },
        "zip": {
          "type": "string",
          "description": "5-digit zip, e.g. \"66603\""
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500,
          "description": "Only rows within this straight-line distance"
        },
        "drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"
        }
      },
      "additionalProperties": false,
      "description": "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 150,
      "description": "Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)"
    },
    "category": {
      "type": "string",
      "description": "Instead of dataset: every dataset of one kind — \"retail\" (store chains), an industry or subcategory, or a kind of business such as \"restaurant\" or \"bank branch\" (search_datasets names these)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches"
    },
    "relate": {
      "type": "object",
      "additionalProperties": {}
    },
    "order_by": {
      "type": "object",
      "properties": {
        "field": {
          "type": "string"
        },
        "direction": {
          "type": "string",
          "enum": [
            "asc",
            "desc"
          ],
          "description": "Default desc"
        }
      },
      "required": [
        "field"
      ],
      "additionalProperties": false,
      "description": "Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10000,
      "description": "Most rows to buy (max 10,000). Default: every match up to the max. Priced on the rows that actually match, never more."
    },
    "email": {
      "type": "string",
      "format": "email",
      "description": "Buyer email, if known — prefills Checkout and is where the download link is sent"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

query_locations

Return matching rows from one dataset, filtered on ANY of its columns — state/city/county/zip shortcuts plus `where` conditions with numeric comparisons (e.g. [{field:"revenue_amt",op:"gt",value:2000000}]), sorted with `order_by` and paged with `offset`. `near` ({place:"Topeka, KS"}, a zip, or lat+lng, optional radius_miles or drive_minutes) returns the closest rows first with distance_miles, on lists with coordinates — so "10 banks closest to Topeka" is one call for 10 rows. get_dataset lists the columns; count_locations (free) tells you how many rows match and what fetching them costs before you pay. Priced per row in USDC via x402 and settled only after the rows are produced, so a failed call costs nothing. The rate is derived from the dataset: roughly 2x its list price spread over its record count, so a small slice of a big file is cents. By default you get and pay for every matching row, up to 100 to 1,000 rows per call depending on how wide the dataset's rows are (count_locations reports maxRowsPerCall); pass limit for fewer. Call it without payment first: the result is an x402 PaymentRequired quote with the exact amount, and nothing is charged until you retry with payment. Datasets under 5,000 records are not sold by the row — buy those files outright. To cover several chains near one place, pass datasets or category and a total (up to 1,000 rows) instead of dataset: one answer, one price and one file, with source_dataset naming each row's dataset and duplicates removed; inside a combined answer, small datasets are sold by the row too.

Parameters17

dataset	string	optional	Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead.
state	string	optional	Shortcut for where state eq <value>. Two-letter code.
city	string	optional	Shortcut for where city eq <value>
county	string	optional	Shortcut for where county eq <value>
zip	string	optional	Shortcut for where zip eq <value>
where	array	optional	Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds.
near	object	optional	Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted.
area_where	array	optional	Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), ….
area_columns	array	optional	Census facts added to every row as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income". Free.
datasets	array	optional	Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)
category	string	optional	Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these)
exclude	array	optional	With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)
total	integer	optional	With datasets or category: rows to return and pay for across all of them (default every distinct match, up to 1,000), in one payment
relate	object	optional
order_by	object	optional	Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last.
offset	integer	optional	Skip this many matching rows, to page past the first call
limit	integer	optional	Rows to return and pay for. Default: every matching row, up to the most one call can return. That maximum depends on how wide the dataset's rows are, from 100 to 1,000; count_locations reports it as maxRowsPerCall, and a larger limit is reduced to it before pricing.

Raw schema

{
  "type": "object",
  "properties": {
    "dataset": {
      "type": "string",
      "description": "Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead."
    },
    "state": {
      "type": "string",
      "description": "Shortcut for where state eq <value>. Two-letter code."
    },
    "city": {
      "type": "string",
      "description": "Shortcut for where city eq <value>"
    },
    "county": {
      "type": "string",
      "description": "Shortcut for where county eq <value>"
    },
    "zip": {
      "type": "string",
      "description": "Shortcut for where zip eq <value>"
    },
    "where": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "field": {
            "type": "string",
            "description": "Column name as listed by get_dataset (columns[].name), e.g. revenue_amt"
          },
          "op": {
            "type": "string",
            "enum": [
              "eq",
              "ne",
              "gt",
              "gte",
              "lt",
              "lte",
              "in",
              "contains",
              "starts_with",
              "is_blank",
              "not_blank"
            ],
            "description": "eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."
          },
          "value": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "number"
              },
              {
                "type": "boolean"
              },
              {
                "type": "array",
                "items": {
                  "type": [
                    "string",
                    "number"
                  ]
                }
              }
            ]
          }
        },
        "required": [
          "field",
          "op"
        ],
        "additionalProperties": false
      },
      "maxItems": 12,
      "description": "Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds."
    },
    "near": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string",
          "description": "City or town with state, e.g. \"Topeka, KS\". A neighborhood or misspelling falls back to the nearest Census place name in that state, and the answer says which."
        },
        "zip": {
          "type": "string",
          "description": "5-digit zip, e.g. \"66603\""
        },
        "lat": {
          "type": "number"
        },
        "lng": {
          "type": "number"
        },
        "radius_miles": {
          "type": "number",
          "exclusiveMinimum": 0,
          "maximum": 500,
          "description": "Only rows within this straight-line distance"
        },
        "drive_minutes": {
          "type": "integer",
          "minimum": 5,
          "maximum": 60,
          "description": "Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"
        }
      },
      "additionalProperties": false,
      "description": "Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."
    },
    "area_where": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
    },
    "area_columns": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 6,
      "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 150,
      "description": "Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file)"
    },
    "category": {
      "type": "string",
      "description": "Instead of dataset: every dataset of one kind — \"retail\" (store chains), an industry or subcategory, or a kind of business such as \"restaurant\" or \"bank branch\" (search_datasets names these)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)"
    },
    "total": {
      "type": "integer",
      "minimum": 1,
      "maximum": 1000,
      "description": "With datasets or category: rows to return and pay for across all of them (default every distinct match, up to 1,000), in one payment"
    },
    "relate": {
      "type": "object",
      "additionalProperties": {}
    },
    "order_by": {
      "type": "object",
      "properties": {
        "field": {
          "type": "string"
        },
        "direction": {
          "type": "string",
          "enum": [
            "asc",
            "desc"
          ],
          "description": "Default desc"
        }
      },
      "required": [
        "field"
      ],
      "additionalProperties": false,
      "description": "Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "maximum": 10000,
      "description": "Skip this many matching rows, to page past the first call"
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 1000,
      "description": "Rows to return and pay for. Default: every matching row, up to the most one call can return. That maximum depends on how wide the dataset's rows are, from 100 to 1,000; count_locations reports it as maxRowsPerCall, and a larger limit is reduced to it before pricing."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

buy_dataset

Buy an ENTIRE dataset outright and get a permanent download link for the CSV. Pays once in USDC on Base, at the same list price a human pays by card — no account and no checkout page.\n\nPrefer this over repeated query_locations calls whenever you want most of a file. Metered queries are priced per row and deliberately cost more than the file if you assemble it that way, so past a few hundred rows buying outright is both cheaper and complete. get_dataset (free) gives the price and record count first.

Parameters1

dataset

string

required

Dataset slug, e.g. generac-dealers. Use search_datasets first.

Raw schema

{
  "type": "object",
  "properties": {
    "dataset": {
      "type": "string",
      "description": "Dataset slug, e.g. generac-dealers. Use search_datasets first."
    }
  },
  "required": [
    "dataset"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

README.md9 KBView on GitHub

LocationLists MCP server

No connector needed: any AI assistant can answer a location question by opening https://locationlists.com/find?q= — counts, a preview, a price and a card checkout link. Connect the MCP for repeat use or wallet payments.

Answer from a link (no connector)

Open the link. No account, no key, no setup. The answer comes back with how many locations match, a few real rows, the price and a checkout link.

100 retail stores within 25 miles of Los Angeles: https://locationlists.com/find?category=retail&near=Los+Angeles+CA&radius=25&total=100
Dental practices in Richmond, VA: https://locationlists.com/find?dataset=dental-practice-list&city=Richmond&state=VA
STIHL dealers within a 30-minute drive of zip 23219: https://locationlists.com/find?dataset=stihl-dealers&near=23219&drive=30

Or set no parameters at all and put the question itself in q=, in plain words.

Everything below is for repeat use and wallet payments.

LocationLists MCP server

Listed on Smithery · Glama · Official MCP Registry · x402scan

Business location data for AI agents. Search, sample, count and buy ready-to-use lists of US business locations: 966 datasets compiled from each brand's official store locator or the public register that publishes the data.

Filter by city, state, zip, a radius around any place, or any column in the file. Buy a full CSV by card, or let an agent pay per row or buy the whole file in USDC on Base via x402, with no account or API key. A failed or empty query costs nothing.

This repository holds the registry listing (server.json) and client setup notes for the hosted LocationLists MCP server. The server runs at locationlists.com; there is nothing to install or run locally.


Endpoint	`https://locationlists.com/mcp`
Transport	Streamable HTTP, stateless, JSON responses
Auth	None
Payments	Stripe Checkout (people) or x402, USDC on Base (agents)
Registry name	`io.github.kylehawke-stack/locationlists`
Read-only profile	`https://locationlists.com/mcp/chatgpt` (search, dataset, sample, count only; no checkout)

What is in the catalog

Figures are read from the live catalog.json (generated 2026-09-16):

966 datasets plus 2 bundles, 15,184,612 US business locations in total (overlapping slices counted once)
Categories: Breakfast, Equipment, Financial, Furniture, Government, Grills, Hardware, Healthcare, Industrial, Mattresses, Nonprofits, Other Transactions, Outdoor Furniture, Retail
Each dataset is compiled from the official locator, association directory or public register that publishes it
One-time prices from $9 to $1,299 per dataset, by record count

Tools

All nine tools are on /mcp. Tools carry readOnlyHint / destructiveHint / openWorldHint annotations. initialize returns serverInfo.name = "LocationLists".

Free tools:

Tool	Arguments	What it does
`search_datasets`	`query?` string, `category?` enum, `limit?` int (≤50)	Find datasets by brand, product line, location type or category. Returns slug, name, record count, coverage and page URL.
`get_dataset`	`slug` string	Full record: fields with descriptions, record and state counts, coverage, refresh cadence, the real last-modified date of the file, FAQs, sample URL and page URL.
`get_sample`	`slug` string, `rows?` int (≤10)	Up to 10 real rows from the live file, spread across the dataset, as JSON plus CSV text.
`count_locations`	`dataset` string, `state?`, `city?`, `county?`, `zip?`, `where?` array	How many rows match a filter on geography and any other column (e.g. `revenue_amt gt 2000000`), and what fetching them would cost.
`get_quote`	`slugs` string[]	Line-item prices and total for one or more datasets; points out a bundle if it covers several requested brands for less.

Card checkout (Stripe):

Tool	Arguments	What it does
`create_checkout`	`slug` string, `email?` string	Opens a Stripe Checkout session for one dataset and returns the payment URL and session id. Does not charge anything by itself.
`check_order`	`sessionId` string	Reports whether a Checkout session is paid and, if so, returns the permanent download link.

Agent payments (x402, USDC on Base):

Tool	Arguments	What it does
`query_locations`	`dataset` string, `state?`, `city?`, `county?`, `zip?`, `where?` array, `order_by?` object, `offset?` int, `limit?` int (≤100)	Rows from one dataset filtered on any column, sorted and paged. Priced per row and settled only after the rows are produced. Datasets under 5,000 records are not sold by the row.
`buy_dataset`	`dataset` string	Buy a whole dataset outright and get a permanent download link for the CSV, at the same list price a card buyer pays.

Agents can pay with x402

query_locations and buy_dataset use x402, the HTTP 402 payment protocol. An agent that holds USDC on Base can buy data with no account, API key or checkout page:

Call the tool without payment. The server answers with the exact price and x402 payment requirements (USDC on Base mainnet, settled through the Coinbase CDP facilitator).
Sign the payment with the agent's wallet and call the tool again with the payment attached.
The server returns the rows (query_locations) or the permanent download link (buy_dataset). Per-row queries settle only after the rows are produced, so a failed query costs nothing.

Pricing rules the agent can rely on:

count_locations is free and reports how many rows a filter matches and what they would cost, before any payment.
Per-row prices are set per dataset at roughly twice the list price spread over its record count, so a small slice of a large file costs cents. You pay for the limit you request.
Past a few hundred rows, buy_dataset is cheaper than assembling the file row by row, and it is complete.

Clients without a wallet can use get_quote → create_checkout → check_order and pay by card instead.

Client setup

Claude Code

bash

claude mcp add --transport http locationlists https://locationlists.com/mcp

Cursor (`~/.cursor/mcp.json`)

json

{
  "mcpServers": {
    "locationlists": {
      "url": "https://locationlists.com/mcp"
    }
  }
}

VS Code (`.vscode/mcp.json`)

json

{
  "servers": {
    "locationlists": {
      "type": "http",
      "url": "https://locationlists.com/mcp"
    }
  }
}

Claude.ai and ChatGPT

Claude.ai: Settings → Connectors → Add custom connector → URL https://locationlists.com/mcp, no OAuth.
ChatGPT: Settings → Connectors (developer mode) → add https://locationlists.com/mcp/chatgpt, no auth. The read-only profile is used there because in-chat purchase of digital goods is not permitted in ChatGPT.

Any MCP client, by hand

bash

curl -s https://locationlists.com/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"search_datasets","arguments":{"query":"bobcat"}}}'

A quick path through the tools: search_datasets {query:"bobcat"} → get_sample {slug:"bobcat-dealers"} → count_locations {dataset:"bobcat-dealers", state:"TX"} → get_quote {slugs:["bobcat-dealers"]} → create_checkout {slug:"bobcat-dealers"} → check_order {sessionId}.

Plain HTTP endpoints (no MCP client needed)

URL	What
`/llms.txt`	Curated map of the site for assistants (llmstxt.org)
`/llms-full.txt`	Every dataset with fields, counts, refresh and price
`/catalog.json`	The same catalog as JSON
`/data/{slug}/dataset.json`	One dataset in full, including the real last-modified date
`/data/{slug}/sample.csv`, `/data/{slug}/sample.json`	10 real rows, no signup
`/data/{slug}/buy?src=<name>`	Redirects to Stripe Checkout for that dataset

Example: bobcat-dealers/dataset.json, bobcat-dealers/sample.csv.

License

The files in this repository (README, server.json) are MIT licensed; see LICENSE. The datasets sold through the server are not covered by that license; they are governed by https://locationlists.com/license.

Install

Tools (16)

search_datasetsrelate_locationscount_by_areacotenancyget_datasetcount_locationsget_samplerequest_list

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://locationlists.com/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "locationlists": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://locationlists.com/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

LocationLists

MCP Server: io.github.kylehawke-stack/locationlists

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

search_datasets

relate_locations

count_by_area

cotenancy

get_dataset

count_locations

get_sample

request_list

send_feedback

email_quote

get_quote

create_checkout

check_order

create_query_checkout

query_locations

buy_dataset

LocationLists MCP server

Answer from a link (no connector)

What is in the catalog

Tools

Agents can pay with x402

Client setup

Claude Code

Cursor (`~/.cursor/mcp.json`)

VS Code (`.vscode/mcp.json`)

Claude.ai and ChatGPT

Any MCP client, by hand

Plain HTTP endpoints (no MCP client needed)

Links

License

Install

Remote endpoint

Related servers

MCP Server: io.github.kylehawke-stack/locationlists

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

search_datasets

relate_locations

count_by_area

cotenancy

get_dataset

count_locations

get_sample

request_list

send_feedback

email_quote

get_quote

create_checkout

check_order

create_query_checkout

query_locations

buy_dataset

LocationLists MCP server

Answer from a link (no connector)

What is in the catalog

Tools

Agents can pay with x402

Client setup

Claude Code

Cursor (~/.cursor/mcp.json)

VS Code (.vscode/mcp.json)

Claude.ai and ChatGPT

Any MCP client, by hand

Plain HTTP endpoints (no MCP client needed)

Links

License

Install

Remote endpoint

Related servers

Cursor (`~/.cursor/mcp.json`)

VS Code (`.vscode/mcp.json`)