Fodda Knowledge Graphs

Official30 toolsLive

by piers-fawkes · TypeScript

Expert-curated knowledge, brand, research & earnings intelligence — 46 tools, 220+ graphs.

ai.fodda/mcp-server (Model Context Protocol)

The MCP server provides expert-curated knowledge graphs for AI agents, covering retail, beauty, sports, and partner datasets. It is described as “brand, research & earnings intelligence” and supports 220+ graphs via the Model Context Protocol (MCP). The server is associated with the npm package fodda-mcp and an MCP registry entry.

🛠️ Key Features

Expert-curated knowledge graphs for AI agents
Brand, research & earnings intelligence
PSFK retail, beauty, sports, and partner datasets
47 tools (source lists both 47 and toolCount=30)

🚀 Use Cases

Retail, beauty, and sports trend/research intelligence for agents
Knowledge-graph-backed question answering and analysis for MCP clients

⚡ Developer Benefits

Integrates via MCP (Model Context Protocol)
Access to a large graph set (“220+ graphs”)

⚠️ Limitations

Tool count is inconsistent in provided data (47 tools vs toolCount: 30).

Topics

agenticai-agentsclaudecopilotcursorknowledge-graphmcpmodel-context-protocolresearchretailtrend-intelligence

Captured live from the server via tools/list.

get_my_account

Check the current user's account status: API call balance, plan, enabled/disabled graphs, and profile info. Use when the user asks "how many API calls do I have?", "what plan am I on?", "what graphs can I access?", or similar account questions. Returns live data — not cached from session start.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

list_graphs

List all knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts. Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools. Deprecated: waldo, psfk (use retail/tech/food/travel/fashion/beauty/sports instead).

Parameters1

userId

string

optional

Optional user identifier. Authenticated users are identified automatically via API key. For trial users, this helps track usage.

Raw schema

{
  "type": "object",
  "properties": {
    "userId": {
      "description": "Optional user identifier. Authenticated users are identified automatically via API key. For trial users, this helps track usage.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

list_analysts

List available Synthetic Analysts — named expert personas grounded in specific knowledge graphs. Each analyst has a unique voice, methodology, and domain expertise that cannot be replicated by web search. Use when user asks to "talk to" or "consult" an expert, or when you need specialist depth on culture, strategy, or innovation topics.

Parameters1

userId

string

optional

Optional user identifier.

Raw schema

{
  "type": "object",
  "properties": {
    "userId": {
      "description": "Optional user identifier.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

search_graph

Find trends, signals, and expert insights across 100+ curated knowledge graphs covering retail, beauty, tech, food, travel, sports, and 30+ specialist domains. Returns trend data with cited evidence, source attribution, and lifecycle stage (emerging/building/mature/fading) — not generic web summaries. If graphId is omitted, searches ALL accessible graphs in parallel (recommended default). Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts, or any topic where curated expert intelligence outperforms web search.

Parameters7

graphId	string	optional	Optional graph ID. If omitted, searches ALL accessible graphs. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'delta/the-connection-index'
query	string	required	The search query. Location terms are auto-detected and used to filter results geographically.
userId	string	optional	Optional user identifier for trial usage tracking.
limit	number	optional	Maximum number of results (default 10, max 50)
use_semantic	boolean	optional	Whether to use semantic search (default true)
include_evidence	boolean	optional	If true, batch-fetch supporting evidence articles inline with results. Default: true.
skip_skills	boolean	optional	If true, skip applying any enabled skills (Paralogy, Igloo, etc.) for this query only. Use when the user says "without skills", "skip Paralogy", or "just the raw results". Default: false.

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "description": "Optional graph ID. If omitted, searches ALL accessible graphs. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'delta/the-connection-index'",
      "type": "string"
    },
    "query": {
      "type": "string",
      "description": "The search query. Location terms are auto-detected and used to filter results geographically."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "limit": {
      "description": "Maximum number of results (default 10, max 50)",
      "type": "number"
    },
    "use_semantic": {
      "description": "Whether to use semantic search (default true)",
      "type": "boolean"
    },
    "include_evidence": {
      "description": "If true, batch-fetch supporting evidence articles inline with results. Default: true.",
      "type": "boolean"
    },
    "skip_skills": {
      "description": "If true, skip applying any enabled skills (Paralogy, Igloo, etc.) for this query only. Use when the user says \"without skills\", \"skip Paralogy\", or \"just the raw results\". Default: false.",
      "type": "boolean"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_neighbors

Discover what's connected to a specific trend — related brands, technologies, locations, and cross-domain links that search alone wouldn't surface. Returns curated editorial connections between trends that web search cannot provide. Use after search_graph to map the territory around a trend, find which brands are connected, or understand cross-domain relationships. Requires node_id from a prior search_graph result.

Parameters7

graphId	string	required	The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
seed_node_ids	array	required	Array of node IDs to start traversal from. MUST be actual node_id values from a prior search_graph result (e.g. ["2507.0"]). Node IDs are NOT sequential integers — do NOT guess or invent IDs like "1", "2", "3". Always call search_graph first to obtain valid IDs.
userId	string	optional	Optional user identifier for trial usage tracking.
relationship_types	array	optional	Filter by relationship types: 'EVIDENCED_BY', 'RELATED_TO', 'SEMANTICALLY_SIMILAR', 'ASSOCIATED_BRAND', 'MENTIONS_BRAND', 'IN_LOCATION'
direction	string	optional	Traversal direction: 'out' (default) follows outgoing edges, 'in' follows incoming edges
depth	number	optional	Traversal depth (default 1, max 2)
limit	number	optional	Maximum results (default 50)

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "type": "string",
      "description": "The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'"
    },
    "seed_node_ids": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Array of node IDs to start traversal from. MUST be actual node_id values from a prior search_graph result (e.g. [\"2507.0\"]). Node IDs are NOT sequential integers — do NOT guess or invent IDs like \"1\", \"2\", \"3\". Always call search_graph first to obtain valid IDs."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "relationship_types": {
      "description": "Filter by relationship types: 'EVIDENCED_BY', 'RELATED_TO', 'SEMANTICALLY_SIMILAR', 'ASSOCIATED_BRAND', 'MENTIONS_BRAND', 'IN_LOCATION'",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "direction": {
      "description": "Traversal direction: 'out' (default) follows outgoing edges, 'in' follows incoming edges",
      "type": "string",
      "enum": [
        "in",
        "out"
      ]
    },
    "depth": {
      "description": "Traversal depth (default 1, max 2)",
      "type": "number"
    },
    "limit": {
      "description": "Maximum results (default 50)",
      "type": "number"
    }
  },
  "required": [
    "graphId",
    "seed_node_ids"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_evidence

Get the source articles, case studies, and statistics behind a specific trend — with full citations and publisher attribution. Each item includes source URL, location, brand names, publication date, category, and a formatted citation. Use after search_graph when you need the supporting proof behind a trend. This is a direct lookup by trend ID — not a text search tool.

Parameters4

graphId	string	required	The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
for_node_id	string	required	The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name.
userId	string	optional	Optional user identifier for trial usage tracking.
top_k	number	optional	Number of evidence items to return (default 5)

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "type": "string",
      "description": "The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'"
    },
    "for_node_id": {
      "type": "string",
      "description": "The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "top_k": {
      "description": "Number of evidence items to return (default 5)",
      "type": "number"
    }
  },
  "required": [
    "graphId",
    "for_node_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_node

Get the full profile of a specific trend — detailed description, lifecycle stage (emerging/building/mature), signal strength, geographic scope, and all properties. Use when you need deeper detail on a single trend after search_graph returned a summary. Requires node_id from a prior search_graph result.

Parameters3

graphId	string	required	The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
nodeId	string	required	The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name.
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "type": "string",
      "description": "The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'"
    },
    "nodeId": {
      "type": "string",
      "description": "The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "graphId",
    "nodeId"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_label_values

List all brands, locations, technologies, audiences, or trends within a specific knowledge graph. Use to explore what a graph contains — e.g., "what brands are in the retail graph?" or "what locations does the fashion graph cover?". To get a complete list of every trend in a graph, call with label="Trend" — this returns the full deterministic list, useful for industry-report graphs where search may return partial results.

Parameters4

graphId	string	required	The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
label	string	required	The label to fetch values for (e.g., 'Brand', 'Location', 'Technology', 'Audience', 'RetailerType', 'Trend')
userId	string	optional	Optional user identifier for trial usage tracking.
property	string	optional	Optional property to return values for. Defaults vary by label.

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "type": "string",
      "description": "The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'"
    },
    "label": {
      "type": "string",
      "description": "The label to fetch values for (e.g., 'Brand', 'Location', 'Technology', 'Audience', 'RetailerType', 'Trend')"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "property": {
      "description": "Optional property to return values for. Defaults vary by label.",
      "type": "string"
    }
  },
  "required": [
    "graphId",
    "label"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

discover_adjacent_trends

Find trends similar to one you've already found — surfaces unexpected cross-domain connections that keyword search would miss. Returns scored similarity matches and optionally editorial links across graphs. Use to expand research briefs, discover cross-industry parallels, or map the territory around a strong signal. This leverages Fodda's proprietary similarity index across all knowledge graphs.

Parameters6

graphId	string	required	The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
trend_id	string	required	The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name.
userId	string	optional	Optional user identifier for trial usage tracking.
min_score	number	optional	Minimum similarity score threshold (0-1). Default: 0.80
limit	number	optional	Maximum number of adjacent trends to return. Default: 10
include_editorial	boolean	optional	If true, also include editorially linked trends. Default: false

Raw schema

{
  "type": "object",
  "properties": {
    "graphId": {
      "type": "string",
      "description": "The graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'generative-realities', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'"
    },
    "trend_id": {
      "type": "string",
      "description": "The node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "min_score": {
      "description": "Minimum similarity score threshold (0-1). Default: 0.80",
      "type": "number"
    },
    "limit": {
      "description": "Maximum number of adjacent trends to return. Default: 10",
      "type": "number"
    },
    "include_editorial": {
      "description": "If true, also include editorially linked trends. Default: false",
      "type": "boolean"
    }
  },
  "required": [
    "graphId",
    "trend_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

brand_tracker

Build a complete Brand Intelligence Profile by searching ALL knowledge graphs for a specific brand. Returns trend footprint (which trends the brand appears in), competitive landscape (co-occurring brands ranked by overlap), cross-graph presence, evidence timeline, lifecycle distribution, and bundled supplemental signals (Google Trends, Wikipedia, Amazon, earnings). Use when the query is about a specific company or brand — "What is Nike doing?", "Patagonia's innovation strategy", "How is Apple positioned?". This aggregates intelligence that would require dozens of separate web searches to assemble.

Parameters5

brand_name	string	required	The brand name to look up (e.g. 'Nike', 'Adidas', 'Apple'). Case-insensitive.
userId	string	optional	Optional user identifier for trial usage tracking.
graph_ids	array	optional	Optional: specific graph IDs to search. If omitted, searches ALL accessible graphs.
include_evidence	boolean	optional	If true (default), include individual evidence items. Set to false for summary-only.
max_evidence	number	optional	Maximum evidence items per graph. Default: 10. Max: 25.

Raw schema

{
  "type": "object",
  "properties": {
    "brand_name": {
      "type": "string",
      "description": "The brand name to look up (e.g. 'Nike', 'Adidas', 'Apple'). Case-insensitive."
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    },
    "graph_ids": {
      "description": "Optional: specific graph IDs to search. If omitted, searches ALL accessible graphs.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "include_evidence": {
      "description": "If true (default), include individual evidence items. Set to false for summary-only.",
      "type": "boolean"
    },
    "max_evidence": {
      "description": "Maximum evidence items per graph. Default: 10. Max: 25.",
      "type": "number"
    }
  },
  "required": [
    "brand_name"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_supplemental_context

Get real-time market data from 80+ authoritative sources in a single call — economic indicators, trade statistics, consumer demand signals, research trends, demographics, and more. The server automatically selects the most relevant sources for your query. Use AFTER graph searches to add quantitative context, or standalone for market intelligence. Returns categorized data blocks (demand_signals, economic_context, market_data, research_signals, demographic_context) with source attribution for citations. 5 API calls per standalone use.

Parameters5

query	string	required	The topic or query to get supplemental data for (e.g., 'sustainable packaging', 'tequila spirits market', 'Gen Z beauty')
domain	string	optional	Domain hint to improve source routing: 'retail', 'beauty', 'fashion', 'sports', 'food', 'technology', 'culture', 'travel', 'design'. If omitted, inferred from query.
brands	array	optional	Brand names to include in demand/product lookups (e.g., ['Nike', 'Adidas']). Triggers Google Trends comparison and Amazon product search.
graph_ids	array	optional	Graph IDs from prior search results — helps refine domain inference.
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The topic or query to get supplemental data for (e.g., 'sustainable packaging', 'tequila spirits market', 'Gen Z beauty')"
    },
    "domain": {
      "description": "Domain hint to improve source routing: 'retail', 'beauty', 'fashion', 'sports', 'food', 'technology', 'culture', 'travel', 'design'. If omitted, inferred from query.",
      "type": "string"
    },
    "brands": {
      "description": "Brand names to include in demand/product lookups (e.g., ['Nike', 'Adidas']). Triggers Google Trends comparison and Amazon product search.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "graph_ids": {
      "description": "Graph IDs from prior search results — helps refine domain inference.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

check_supplemental_status

Check if market data gathering is complete and retrieve the results. Call this after get_supplemental_context — poll every 5-10 seconds until status is COMPLETE or FAILED.

Parameters1

job_id

string

required

The Job ID returned by get_supplemental_context

Raw schema

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string",
      "description": "The Job ID returned by get_supplemental_context"
    }
  },
  "required": [
    "job_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_domain_intelligence

Search PSFK-curated domain graphs (retail, beauty, fashion, sports, consumer electronics, F&B) for trend intelligence with bundled evidence. No graph ID needed — searches all relevant domain graphs in parallel. Returns expert-curated trends with categorized evidence (statistics, case studies, analysis, interviews) and source attribution. Use for broad industry trend research, sector analysis, or when the query spans multiple consumer categories. Preferred over web search for trend-level intelligence because results are editorially structured, not algorithmically ranked.

Parameters6

query	string	required	Natural language search query (e.g., 'sustainable packaging trends', 'Gen Z beauty habits')
limit	number	optional	Max trends to return (default: 10, max: 50)
include_evidence	boolean	optional	Bundle evidence for each trend (default: true)
max_evidence_per_trend	number	optional	Evidence items per trend (default: 5, max: 20)
min_score	number	optional	Minimum relevance threshold (default: 0.6)
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural language search query (e.g., 'sustainable packaging trends', 'Gen Z beauty habits')"
    },
    "limit": {
      "description": "Max trends to return (default: 10, max: 50)",
      "type": "number"
    },
    "include_evidence": {
      "description": "Bundle evidence for each trend (default: true)",
      "type": "boolean"
    },
    "max_evidence_per_trend": {
      "description": "Evidence items per trend (default: 5, max: 20)",
      "type": "number"
    },
    "min_score": {
      "description": "Minimum relevance threshold (default: 0.6)",
      "type": "number"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_expert_intelligence

Search specialist knowledge graphs built by named strategists and industry leaders — contains proprietary analysis, expert interviews, and high-density statistics not available via web search. No graph ID needed — searches all expert graphs in parallel. Use when the query requires specialist depth, named-expert perspectives, or strategic frameworks beyond mainstream coverage. Expert graphs cover domains like macro strategy, wayfinding, design innovation, SXSW insights, and sector-specific research reports.

Parameters6

query	string	required	Natural language search query (e.g., 'tequila spirits market', 'future of work')
limit	number	optional	Max trends to return (default: 10, max: 50)
include_evidence	boolean	optional	Bundle evidence for each trend (default: true)
max_evidence_per_trend	number	optional	Evidence items per trend (default: 5, max: 20)
min_score	number	optional	Minimum relevance threshold (default: 0.6)
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural language search query (e.g., 'tequila spirits market', 'future of work')"
    },
    "limit": {
      "description": "Max trends to return (default: 10, max: 50)",
      "type": "number"
    },
    "include_evidence": {
      "description": "Bundle evidence for each trend (default: true)",
      "type": "boolean"
    },
    "max_evidence_per_trend": {
      "description": "Evidence items per trend (default: 5, max: 20)",
      "type": "number"
    },
    "min_score": {
      "description": "Minimum relevance threshold (default: 0.6)",
      "type": "number"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_report_intelligence

Search industry report knowledge graphs for published research findings, market forecasts, and quantitative projections from organizations like DHL, PwC, Delta, and specialist research firms. Returns structured findings with bundled evidence — not raw PDFs or summaries, but editorially extracted trend data with source attribution. No graph ID needed. Use for market sizing, competitive landscape analysis, and data-heavy research where published report intelligence is more authoritative than web search results.

Parameters6

query	string	required	Natural language search query (e.g., 'luxury resale market size', 'electric vehicle adoption rates')
limit	number	optional	Max trends to return (default: 10, max: 50)
include_evidence	boolean	optional	Bundle evidence for each trend (default: true)
max_evidence_per_trend	number	optional	Evidence items per trend (default: 5, max: 20)
min_score	number	optional	Minimum relevance threshold (default: 0.6)
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural language search query (e.g., 'luxury resale market size', 'electric vehicle adoption rates')"
    },
    "limit": {
      "description": "Max trends to return (default: 10, max: 50)",
      "type": "number"
    },
    "include_evidence": {
      "description": "Bundle evidence for each trend (default: true)",
      "type": "boolean"
    },
    "max_evidence_per_trend": {
      "description": "Evidence items per trend (default: 5, max: 20)",
      "type": "number"
    },
    "min_score": {
      "description": "Minimum relevance threshold (default: 0.6)",
      "type": "number"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

search_statistics

Find specific numbers, market sizes, growth rates, and quantitative data points across Fodda's knowledge graphs. Each result links back to the expert trend it supports. Use when a question asks for specific statistics — try this BEFORE supplemental data tools, as Fodda's experts may have already curated the answer. Works on ALL graphs — domain, expert, and report graphs. Search multiple graphs for best coverage.

Parameters6

graph_id	string	required	Graph ID to search. Works on ALL graphs — PSFK curated ('retail', 'fashion', 'beauty', 'sports', 'sic', 'ce-design', 'pew') AND expert graphs. Search across multiple graphs for best coverage.
query	string	required	What data to search for (e.g., 'luxury resale market size', 'secondhand clothing sales volume', 'Gen Z spending behavior')
limit	number	optional	Max results to return (default: 10, max: 50)
min_score	number	optional	Minimum relevance threshold, 0-1 (default: 0.60). Use 0.60 for broad queries, 0.70+ only for precise data lookups.
include_signals	boolean	optional	Also include Signal nodes (case studies, brand examples). Default: false
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "graph_id": {
      "type": "string",
      "description": "Graph ID to search. Works on ALL graphs — PSFK curated ('retail', 'fashion', 'beauty', 'sports', 'sic', 'ce-design', 'pew') AND expert graphs. Search across multiple graphs for best coverage."
    },
    "query": {
      "type": "string",
      "description": "What data to search for (e.g., 'luxury resale market size', 'secondhand clothing sales volume', 'Gen Z spending behavior')"
    },
    "limit": {
      "description": "Max results to return (default: 10, max: 50)",
      "type": "number"
    },
    "min_score": {
      "description": "Minimum relevance threshold, 0-1 (default: 0.60). Use 0.60 for broad queries, 0.70+ only for precise data lookups.",
      "type": "number"
    },
    "include_signals": {
      "description": "Also include Signal nodes (case studies, brand examples). Default: false",
      "type": "boolean"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "graph_id",
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

search_insights

Find expert quotes, editorial analysis, and strategic perspectives on a topic — sourced from named strategists and industry leaders. Returns categorized evidence (metrics, quotes, interpretations, signals) with source attribution and parent trend context. Works on ALL graphs. Use when you need authoritative voices, strategic framing, or analytical depth that web search cannot provide.

Parameters6

graph_id	string	required	Graph ID to search. Works on ALL graphs — PSFK curated ('retail', 'sic', 'beauty', 'sports', 'fashion', 'ce-design', 'pew') AND expert graphs. Search across multiple graphs for best coverage.
query	string	required	Natural language search query. E.g. 'expert views on Gen Z luxury' or 'resale market statistics'
types	string	optional	Comma-separated evidence types to search: metric, quote, interpretation, signal, or 'all' (default: 'metric,quote,interpretation')
limit	number	optional	Max results to return (default: 10, max: 50)
min_score	number	optional	Minimum relevance threshold 0-1 (default: 0.60). Use 0.60 for broad queries, 0.70+ for precise lookups.
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "graph_id": {
      "type": "string",
      "description": "Graph ID to search. Works on ALL graphs — PSFK curated ('retail', 'sic', 'beauty', 'sports', 'fashion', 'ce-design', 'pew') AND expert graphs. Search across multiple graphs for best coverage."
    },
    "query": {
      "type": "string",
      "description": "Natural language search query. E.g. 'expert views on Gen Z luxury' or 'resale market statistics'"
    },
    "types": {
      "description": "Comma-separated evidence types to search: metric, quote, interpretation, signal, or 'all' (default: 'metric,quote,interpretation')",
      "type": "string"
    },
    "limit": {
      "description": "Max results to return (default: 10, max: 50)",
      "type": "number"
    },
    "min_score": {
      "description": "Minimum relevance threshold 0-1 (default: 0.60). Use 0.60 for broad queries, 0.70+ for precise lookups.",
      "type": "number"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "graph_id",
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_earnings_intelligence

Query earnings call intelligence across companies, industries, or sectors. Returns structured evidence from public company earnings calls — management commentary, guidance, key topics, and analyst Q&A. Use for cross-company comparisons ("what are hotel companies saying about labor costs?"), industry-level queries ("earnings intelligence for consumer electronics"), or explicit earnings requests. For single-brand earnings, use brand_tracker instead — it includes earningsIntelligence automatically. Results include a source field: "knowledge_graph" (high confidence, structured Neo4j data) or "web_supplemental" (backfilled via web search). 5 API calls per use.

Parameters9

ticker	string	optional	Company stock ticker (e.g., 'NKE', 'LVMUY', 'HLT'). At least one filter required.
brand	string	optional	Brand name for fuzzy matching (e.g., 'Nike', 'Marriott')
industry	string	optional	Industry filter (e.g., 'hotels', 'sportswear', 'consumer electronics')
sector	string	optional	Sector filter (e.g., 'retail', 'technology', 'travel')
search	string	optional	Free text search in earnings summaries (e.g., 'labor costs', 'tariff guidance', 'AI investment')
dateFrom	string	optional	ISO date range start (e.g., '2025-01-01')
dateTo	string	optional	ISO date range end (e.g., '2026-06-01')
limit	integer	optional	Max results to return (default 20, max 50)
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "ticker": {
      "description": "Company stock ticker (e.g., 'NKE', 'LVMUY', 'HLT'). At least one filter required.",
      "type": "string"
    },
    "brand": {
      "description": "Brand name for fuzzy matching (e.g., 'Nike', 'Marriott')",
      "type": "string"
    },
    "industry": {
      "description": "Industry filter (e.g., 'hotels', 'sportswear', 'consumer electronics')",
      "type": "string"
    },
    "sector": {
      "description": "Sector filter (e.g., 'retail', 'technology', 'travel')",
      "type": "string"
    },
    "search": {
      "description": "Free text search in earnings summaries (e.g., 'labor costs', 'tariff guidance', 'AI investment')",
      "type": "string"
    },
    "dateFrom": {
      "description": "ISO date range start (e.g., '2025-01-01')",
      "type": "string"
    },
    "dateTo": {
      "description": "ISO date range end (e.g., '2026-06-01')",
      "type": "string"
    },
    "limit": {
      "description": "Max results to return (default 20, max 50)",
      "type": "integer",
      "minimum": -9007199254740991,
      "maximum": 9007199254740991
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_earnings_divergence

Detect divergence between analyst concerns and management responses in earnings calls. Surfaces where executives are deflecting, reframing, or avoiding specific topics. Premium intelligence — shows the gap between what Wall Street is worried about and what companies are saying. Results include deflected topics, concern-vs-response framing, and connections to Fodda trends via :VALIDATES edges. Use for "where are executives deflecting?" or "divergence in [sector] earnings." 5 API calls per use.

Parameters7

sector	string	optional	Sector filter (e.g., 'retail', 'technology', 'travel')
industry	string	optional	Industry filter (e.g., 'hotels', 'sportswear', 'luxury')
search	string	optional	Free text search (e.g., 'tariffs', 'AI capex', 'margin erosion')
dateFrom	string	optional	ISO date range start
dateTo	string	optional	ISO date range end
limit	integer	optional	Max results to return (default 10, max 25)
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "sector": {
      "description": "Sector filter (e.g., 'retail', 'technology', 'travel')",
      "type": "string"
    },
    "industry": {
      "description": "Industry filter (e.g., 'hotels', 'sportswear', 'luxury')",
      "type": "string"
    },
    "search": {
      "description": "Free text search (e.g., 'tariffs', 'AI capex', 'margin erosion')",
      "type": "string"
    },
    "dateFrom": {
      "description": "ISO date range start",
      "type": "string"
    },
    "dateTo": {
      "description": "ISO date range end",
      "type": "string"
    },
    "limit": {
      "description": "Max results to return (default 10, max 25)",
      "type": "integer",
      "minimum": -9007199254740991,
      "maximum": 9007199254740991
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

update_user_profile

Save the user's research profile to improve the relevance of future responses. Call this after you understand the user's role, industry, and research needs. The profile persists across sessions — you only need to set it once, then update if their focus changes. Write BEHAVIORAL INSTRUCTIONS, not a bio. Format: one sentence of identity (who they are and how they use Fodda), then numbered directives that change how you synthesize and frame responses. Include: what evidence to prioritize, how to frame conclusions, geographic needs, and output structure preferences. Max 2000 chars per field.

Parameters2

userContext	string	required	Behavioral framing instructions for this person. Format: one sentence of identity, then numbered FRAMING INSTRUCTIONS. Example: "Agency strategist doing time-pressured pitches. (1) Lead with landscape orientation — top 3-5 macro forces. (2) Prioritize commercially validated signals over design concepts. (3) ALWAYS differentiate by geography. (4) Executive-ready framing — concise, pitch-deck-ready. (5) Strongest findings first, not exhaustive lists." Max 2000 chars.
accountContext	string	optional	Description of their company: industry, size, key markets, competitive position, mission. Shared across all users on this account. Max 2000 chars.

Raw schema

{
  "type": "object",
  "properties": {
    "userContext": {
      "type": "string",
      "description": "Behavioral framing instructions for this person. Format: one sentence of identity, then numbered FRAMING INSTRUCTIONS. Example: \"Agency strategist doing time-pressured pitches. (1) Lead with landscape orientation — top 3-5 macro forces. (2) Prioritize commercially validated signals over design concepts. (3) ALWAYS differentiate by geography. (4) Executive-ready framing — concise, pitch-deck-ready. (5) Strongest findings first, not exhaustive lists.\" Max 2000 chars."
    },
    "accountContext": {
      "description": "Description of their company: industry, size, key markets, competitive position, mission. Shared across all users on this account. Max 2000 chars.",
      "type": "string"
    }
  },
  "required": [
    "userContext"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

toggle_graph_preference

Enable or disable any knowledge graph, supplemental data source, or skill for the user. Use this when the user says "Turn off Paralogy", "Enable igloo", "Disable the economics data", or similar. The change is permanent until toggled again.

Parameters3

target_id	string	required	The ID of the graph, skill, or data source to toggle (e.g., "paralogy", "igloo", "retail", "get_bea_spending_snapshot"). Use the exact ID from list_graphs.
enabled	boolean	required	true to enable (turn on), false to disable (turn off).
user_email	string	optional	Optional. Use ONLY when operating as an Admin on behalf of another user to specify their email.

Raw schema

{
  "type": "object",
  "properties": {
    "target_id": {
      "type": "string",
      "description": "The ID of the graph, skill, or data source to toggle (e.g., \"paralogy\", \"igloo\", \"retail\", \"get_bea_spending_snapshot\"). Use the exact ID from list_graphs."
    },
    "enabled": {
      "type": "boolean",
      "description": "true to enable (turn on), false to disable (turn off)."
    },
    "user_email": {
      "description": "Optional. Use ONLY when operating as an Admin on behalf of another user to specify their email.",
      "type": "string"
    }
  },
  "required": [
    "target_id",
    "enabled"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

send_feedback

Forward user feedback, feature requests, complaints, or exit reasons to the Fodda team via email and Slack. Call this whenever a user shares feedback — including when they want to leave, report a problem, or suggest an improvement.

Parameters3

feedback	string	required	The user's feedback, complaint, suggestion, or exit reason
user_email	string	optional	User's email if known (for follow-up)
category	string	optional	Category: 'feedback', 'bug', 'feature_request', 'exit_reason', 'complaint'

Raw schema

{
  "type": "object",
  "properties": {
    "feedback": {
      "type": "string",
      "description": "The user's feedback, complaint, suggestion, or exit reason"
    },
    "user_email": {
      "description": "User's email if known (for follow-up)",
      "type": "string"
    },
    "category": {
      "description": "Category: 'feedback', 'bug', 'feature_request', 'exit_reason', 'complaint'",
      "type": "string"
    }
  },
  "required": [
    "feedback"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

sign_up_free_account

Create a free Fodda Base account (100 API calls/month across ALL knowledge graphs) and send a confirmation email. GUARDRAIL: only call this AFTER the user has explicitly provided their email and asked to create an account — never sign someone up proactively or with an email inferred from earlier context. Can also pass profile fields (name, job_title, company).

Parameters4

email	string	required	User's email address (required)
name	string	optional	User's full name (optional — collect conversationally after signup)
job_title	string	optional	User's job title (optional)
company	string	optional	User's company name (optional)

Raw schema

{
  "type": "object",
  "properties": {
    "email": {
      "type": "string",
      "description": "User's email address (required)"
    },
    "name": {
      "description": "User's full name (optional — collect conversationally after signup)",
      "type": "string"
    },
    "job_title": {
      "description": "User's job title (optional)",
      "type": "string"
    },
    "company": {
      "description": "User's company name (optional)",
      "type": "string"
    }
  },
  "required": [
    "email"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

brainstorm_topic

Explore and brainstorm around a topic using knowledge graph connections. Unlike search (which finds what matches), this tool discovers what CONNECTS — adjacent trends, unexpected cross-domain links, key brands, and geographic hotspots. Use when the user wants to brainstorm, explore adjacencies, find inspiration, or understand the landscape around a topic. Returns a structured brainstorm map with territories to explore.

Parameters3

query	string	required	The topic or theme to brainstorm around (e.g., 'tequila', 'sustainable packaging', 'Gen Z beauty')
depth	number	optional	Traversal depth: 1 (immediate connections) or 2 (connections of connections). Default: 2. Use 1 for focused brainstorms, 2 for wider exploration.
userId	string	optional	Optional user identifier for trial usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The topic or theme to brainstorm around (e.g., 'tequila', 'sustainable packaging', 'Gen Z beauty')"
    },
    "depth": {
      "description": "Traversal depth: 1 (immediate connections) or 2 (connections of connections). Default: 2. Use 1 for focused brainstorms, 2 for wider exploration.",
      "type": "number"
    },
    "userId": {
      "description": "Optional user identifier for trial usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

generate_visual

Create a presentation-ready data visualization from research findings. Available chart types: "cultural_shifts" (From→To transitions), "competitive_compass" (brands on 2 axes), "trend_constellation" (network of related trends), "implication_ladder" (Signal→Trend→So What→Do What), "innovation_pathway" (Now→Near-Term→Future), "opportunity_map" (2×2 white space analysis). Returns a branded SVG that renders directly in the chat.

Parameters2

chart_type	string	required	The type of visualization to generate
data	string	required	JSON string containing the chart data. Structure depends on chart_type. cultural_shifts: {shifts:[{from,to}]}. competitive_compass: {brands:[{name,x,y}], axes:{left,right,top,bottom}}. trend_constellation: {trends:[{name,x,y}], connections:[{from,to,strength}]}. implication_ladder: {signal,trend,so_what,do_what}. innovation_pathway: {now,near_term,future}. opportunity_map: {items:[{name,consumer_desire,market_activity}]}

Raw schema

{
  "type": "object",
  "properties": {
    "chart_type": {
      "type": "string",
      "enum": [
        "cultural_shifts",
        "competitive_compass",
        "trend_constellation",
        "implication_ladder",
        "innovation_pathway",
        "opportunity_map"
      ],
      "description": "The type of visualization to generate"
    },
    "data": {
      "type": "string",
      "description": "JSON string containing the chart data. Structure depends on chart_type. cultural_shifts: {shifts:[{from,to}]}. competitive_compass: {brands:[{name,x,y}], axes:{left,right,top,bottom}}. trend_constellation: {trends:[{name,x,y}], connections:[{from,to,strength}]}. implication_ladder: {signal,trend,so_what,do_what}. innovation_pathway: {now,near_term,future}. opportunity_map: {items:[{name,consumer_desire,market_activity}]}"
    }
  },
  "required": [
    "chart_type",
    "data"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

manage_scheduled_reports

Create, list, cancel, update, pause, or resume scheduled intelligence briefings. Users can set up autonomous research that runs weekly (Mondays) or daily (Mon-Fri) at 9am in their timezone, delivered via email or Slack. Costs 20 API calls per run. Supports topic research or brand intelligence report types.

Parameters10

action	string	required
query	string	optional	For "create": the research query to run
email	string	optional	Email address to deliver reports to
slack_webhook	string	optional	Optional Slack webhook URL for delivery
graphs	array	optional	Specific graph IDs to search. Default: all accessible
schedule_id	string	optional	For cancel/update/pause/resume: the schedule ID
cadence	string	optional	weekly or daily (Mon-Fri). Default: weekly
timezone	string	optional	Delivery timezone for 9am delivery. Default: new_york
report_type	string	optional	topic_research for sector trends, brand_intelligence for competitive tracking
brands	array	optional	For brand_intelligence: brand names to track (e.g., ["Nike", "Patagonia"])

Raw schema

{
  "type": "object",
  "properties": {
    "action": {
      "type": "string",
      "enum": [
        "create",
        "list",
        "cancel",
        "update",
        "pause",
        "resume"
      ]
    },
    "query": {
      "description": "For \"create\": the research query to run",
      "type": "string"
    },
    "email": {
      "description": "Email address to deliver reports to",
      "type": "string"
    },
    "slack_webhook": {
      "description": "Optional Slack webhook URL for delivery",
      "type": "string"
    },
    "graphs": {
      "description": "Specific graph IDs to search. Default: all accessible",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "schedule_id": {
      "description": "For cancel/update/pause/resume: the schedule ID",
      "type": "string"
    },
    "cadence": {
      "description": "weekly or daily (Mon-Fri). Default: weekly",
      "type": "string",
      "enum": [
        "weekly",
        "daily"
      ]
    },
    "timezone": {
      "description": "Delivery timezone for 9am delivery. Default: new_york",
      "type": "string",
      "enum": [
        "london",
        "new_york",
        "san_francisco",
        "sydney"
      ]
    },
    "report_type": {
      "description": "topic_research for sector trends, brand_intelligence for competitive tracking",
      "type": "string",
      "enum": [
        "topic_research",
        "brand_intelligence"
      ]
    },
    "brands": {
      "description": "For brand_intelligence: brand names to track (e.g., [\"Nike\", \"Patagonia\"])",
      "type": "array",
      "items": {
        "type": "string"
      }
    }
  },
  "required": [
    "action"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

read_url

Extract clean text content from any URL. Use this when a user shares a link (competitor site, news article, client brief, trend report) and wants to cross-reference it against Fodda knowledge graphs. Returns structured text ready for analysis. Costs 15 API calls.

Parameters2

url	string	required	The URL to read and extract content from
userId	string	optional	Optional user identifier for usage tracking.

Raw schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "The URL to read and extract content from"
    },
    "userId": {
      "description": "Optional user identifier for usage tracking.",
      "type": "string"
    }
  },
  "required": [
    "url"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

deep_research_topic

Launch an autonomous Deep Research session that combines Fodda knowledge graph intelligence with live web research to produce a comprehensive editorial-quality report. The Research Agent plans its own strategy, searches multiple graphs, validates with institutional data, and synthesizes into a narrative brief with inline source citations. Use for complex, multi-faceted questions that need both curated expert intelligence AND current web context — e.g., strategic briefings, market landscape reports, competitive deep dives. Depth: "light" (20 API calls, faster single-pass) or "heavy" (30 API calls, comprehensive multi-pass with validation).

Parameters4

query	string	required	The research query/topic
graphId	string	optional	Optional specific graph ID to limit the research to
depth	string	optional	Research depth: "light" for faster single-pass (20 API calls), "heavy" for comprehensive multi-pass (30 API calls). Defaults to "light".
userId	string	optional	Optional user identifier.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The research query/topic"
    },
    "graphId": {
      "description": "Optional specific graph ID to limit the research to",
      "type": "string"
    },
    "depth": {
      "description": "Research depth: \"light\" for faster single-pass (20 API calls), \"heavy\" for comprehensive multi-pass (30 API calls). Defaults to \"light\".",
      "type": "string",
      "enum": [
        "light",
        "heavy"
      ]
    },
    "userId": {
      "description": "Optional user identifier.",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

check_research_status

Check if deep research is complete and retrieve the final report. Call this after deep_research_topic — poll every 10 seconds until status is COMPLETE or FAILED.

Parameters1

job_id

string

required

The Job ID returned by deep_research_topic

Raw schema

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string",
      "description": "The Job ID returned by deep_research_topic"
    }
  },
  "required": [
    "job_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

consult_analyst

Consult a named Synthetic Analyst who answers in their expert voice using their curated knowledge graph. Each analyst has a unique methodology, domain expertise, and analytical lens that produces insights distinct from generic search or standard graph queries. Use when the user asks to talk to or consult a specific expert, or when you need a specialist perspective on culture, strategy, or innovation topics. Call list_analysts first to discover available analyst_id values. Responses may include a coverage status (in/adjacent/out), source attribution, and referrals to other expert graphs. Referrals MUST be presented in third-person platform voice (not the expert's voice) with an offer to query the referred graph.

Parameters4

analyst_id	string	required	The analyst ID (e.g., 'ben-dietz-sic')
query	string	required	The question or topic to discuss with the analyst
company	string	optional	Optional company name or stock ticker (e.g., 'Tesla' or 'TSLA') to bind the analyst to a specific brand context.
userId	string	optional	Optional user identifier.

Raw schema

{
  "type": "object",
  "properties": {
    "analyst_id": {
      "type": "string",
      "description": "The analyst ID (e.g., 'ben-dietz-sic')"
    },
    "query": {
      "type": "string",
      "description": "The question or topic to discuss with the analyst"
    },
    "company": {
      "description": "Optional company name or stock ticker (e.g., 'Tesla' or 'TSLA') to bind the analyst to a specific brand context.",
      "type": "string"
    },
    "userId": {
      "description": "Optional user identifier.",
      "type": "string"
    }
  },
  "required": [
    "analyst_id",
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

README.md10 KBView on GitHub

Fodda MCP Server

Expert-curated knowledge graphs for AI agents — PSFK Retail, Beauty, Sports and partner datasets via the Model Context Protocol.

Quick Start

Claude (Web — Pro, Max, Team, Enterprise)

Get your personal MCP URL at app.fodda.ai → Connections (format: https://mcp.fodda.ai/c/<your-token>)
In Claude, go to Settings → Connectors → Add custom connector, paste the URL, and click Add
Start chatting with your Fodda knowledge graphs

Note: Legacy URL query-string parameter authentication is deprecated and returns HTTP 401 with instructions. Get your fresh MCP URL or connect via OAuth at app.fodda.ai.

Claude Code (CLI)

bash

# Connect via OAuth:
claude mcp add --transport http fodda https://mcp.fodda.ai/mcp

# Or with an API key:
claude mcp add --transport http fodda https://mcp.fodda.ai/mcp \
  --header "Authorization: Bearer sk_live_..."

Client Setup Guides

For complete setup guides across all supported clients — Claude Web, Claude Code, Claude Enterprise, ChatGPT, OpenAI Responses API, Cursor, Gemini, and custom agents — visit https://www.fodda.ai/connect.

Available Tools

The Fodda MCP server exposes a rich suite of 30 tools to search, analyze, brainstorm, and visualize trends, expert-curated knowledge graphs, and corporate documents.

Discovery & Search

Tool	Description	Deterministic
`list_graphs`	Discover available knowledge graphs, metadata, and routing instructions.	✅
`search_graph`	Hybrid keyword + semantic search across curated graphs for trend clusters and evidence.	❌
`search_statistics`	Search for specific quantitative metrics, indicators, and numeric data points.	❌
`search_insights`	Search expert quotes, qualitative signals, and professional interpretations.	❌
`get_label_values`	Discover valid values for a node label or category.	✅

Traversal & Graph Operations

Tool	Description	Deterministic
`get_neighbors`	Traverse from seed nodes to discover related concept nodes and links.	✅
`get_node`	Retrieve detailed metadata, properties, and attributes for a single node by ID.	✅
`get_evidence`	Retrieve source signals, articles, citations, and provenance for a trend or node.	✅
`discover_adjacent_trends`	Find semantically similar trends to a given trend node.	✅

Intelligence Domains

Tool	Description	Deterministic
`get_domain_intelligence`	Search all PSFK curated domain graphs (retail, beauty, sports, fashion, consumer electronics, F&B) in parallel.	❌
`get_expert_intelligence`	Query specialist industry graphs built by leading strategists and experts.	❌
`get_report_intelligence`	Search institutional report insights from DHL, PwC, Delta, and other partners.	❌

Earnings & Corporate Insights

Tool	Description	Deterministic
`get_earnings_intelligence`	Query management commentary, business guidance, and Q&A from company earnings calls.	❌
`get_earnings_divergence`	Detect deflections, gaps, and divergence between analyst concerns and executive responses in earnings calls.	❌

Brand & Ideation

Tool	Description	Deterministic
`brand_tracker`	Compile a comprehensive Brand Intelligence Profile across all knowledge graphs.	❌
`brainstorm_topic`	Graph-powered brainstorm map discovering unexpected connections and adjacent territories.	❌

Deep Research & Execution Agents

Tool	Description	Deterministic
`deep_research_topic`	Launch an autonomous research session combining graphs with live web search.	❌
`check_research_status`	Check progress or retrieve the final narrative report of a deep research job.	✅
`get_supplemental_context`	Fetch macro context from up to 10 institutional data sources in a single query.	❌
`check_supplemental_status`	Retrieve output from a supplemental context job.	✅

Synthetic Analyst & Visualization

Tool	Description	Deterministic
`list_analysts`	List available Synthetic Analyst expert personas.	✅
`consult_analyst`	Engage a Synthetic Analyst persona to synthesize answers with specialized voice.	❌
`generate_visual`	Generate branded SVG data visualizations (Cultural Shifts, Competitive Compass, Innovation Pathway, etc.).	❌

Note on Dynamic Partner Skills: When partner skills (such as Paralogy or Igloo) are enabled for a user's account, additional specialized tools (e.g. paralogy_analyze_trends, ideation tools) are dynamically registered on the MCP server and made available automatically.

Note on System Utilities: The server also registers several helper and account management utilities (e.g., get_my_account, toggle_graph_preference, update_user_profile, manage_scheduled_reports, send_feedback) to allow the AI assistant to query subscription status, manage scheduled briefings, or save personalization settings directly.

Discovery Endpoints

Endpoint	Description
`GET /health`	Health check (`{ "status": "ok", "version": "..." }`)
`POST /mcp`	MCP endpoint — call the standard `tools/list` method on an initialized session to retrieve full tool schemas and capabilities

Authentication

Preferred — HTTP header (used by the directory connection and all header-capable clients). Pass your Fodda API key as a Bearer token:

code

Authorization: Bearer sk_live_...

The server also accepts the key via an X-API-Key: sk_live_... header.

In MCP request _meta:

json

{ "_meta": { "authorization": "Bearer sk_live_..." } }

Connection URLs. Web connector clients use personal tokenized connection URLs (https://mcp.fodda.ai/c/<token>) generated in your Connections dashboard. Raw keys in URLs are not accepted.

Configuration

Variable	Description	Default
`PORT`	HTTP server port	`8080`
`FODDA_API_URL`	Upstream API base URL	`https://api.fodda.ai`
`FODDA_MCP_SECRET`	HMAC signing secret for API requests	—
`NODE_ENV`	Environment (`development` / `production`)	`production`

Build & Run

bash

npm install
npm run build
npm start

Self-Hosting

Docker: docker build -t fodda-mcp . && docker run -p 8080:8080 -e PORT=8080 fodda-mcp
Cloud Run: ./deploy_cloud_run.sh
Kubernetes: See deployment/k8s/
Terraform: See deployment/terraform/

MCP Registry

This server is published to the Official MCP Registry as ai.fodda/mcp-server.

bash

# Verify listing
curl "https://registry.modelcontextprotocol.io/v0.1/servers?search=ai.fodda/mcp-server"

Privacy Policy

Full policy: https://www.fodda.ai/privacy

Summary of how the Fodda MCP server handles data (the hosted policy is authoritative):

What we collect. Account information (name, email, organization) to manage your account; usage data — API queries (query text, graph accessed, timestamps) logged for billing, service improvement, and abuse prevention; and standard technical data (IP, browser type, referral URLs). We do not store the full content of AI-generated responses. Your API key authenticates requests and is never returned in tool output.
How it's used. To provide and improve the Services, process billing, enforce rate limits and prevent abuse, communicate support/service updates, and meet legal obligations. Requests are proxied to the Fodda API (https://api.fodda.ai) over TLS.
Third-party sharing. We do not sell your personal information. Data is shared only with service providers (e.g. payment processors, cloud hosting) under confidentiality agreements, or when required by law.
AI model training. Fodda does not use your queries or data to train AI models. Knowledge graphs are expert-curated, not generated from user interactions.
Retention. Account information is retained while your account is active; query logs are retained for billing and analytics. You may request account/data deletion via the contact below.
Security. TLS-encrypted connections, API-key authentication, and secure cloud infrastructure.
Contact. Privacy inquiries: privacy@fodda.ai.

See the full hosted Privacy Policy (last updated June 16, 2026) for the complete, authoritative terms.

Support

Email: hello@fodda.ai
Account & API keys: app.fodda.ai → Connections
Documentation: fodda.ai

To report a security issue, email hello@fodda.ai with "SECURITY" in the subject line.

License

Proprietary — fodda.ai

Install

Tools (30)

get_my_accountlist_graphslist_analystssearch_graphget_neighborsget_evidenceget_nodeget_label_values

Remote endpoint

Streamable HTTP

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

url

https://mcp.fodda.ai/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.fodda.ai/mcp"
      ]
    }
  }
}

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

ai.fodda/mcp-server (Model Context Protocol)

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

get_my_account

list_graphs

list_analysts

search_graph

get_neighbors

get_evidence

get_node

get_label_values

discover_adjacent_trends

brand_tracker

get_supplemental_context

check_supplemental_status

get_domain_intelligence

get_expert_intelligence

get_report_intelligence

search_statistics

search_insights

get_earnings_intelligence

get_earnings_divergence

update_user_profile

toggle_graph_preference

send_feedback

sign_up_free_account

brainstorm_topic

generate_visual

manage_scheduled_reports

read_url

deep_research_topic

check_research_status

consult_analyst

Fodda MCP Server

Quick Start

Claude (Web — Pro, Max, Team, Enterprise)

Claude Code (CLI)

Client Setup Guides

Available Tools

Discovery & Search

Traversal & Graph Operations

Intelligence Domains

Earnings & Corporate Insights

Brand & Ideation

Deep Research & Execution Agents

Synthetic Analyst & Visualization

Discovery Endpoints

Authentication

Configuration

Build & Run

Self-Hosting

MCP Registry

Privacy Policy

Support

License

Install

Remote endpoint

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