io.github.pipeworx-io/dc-dmv MCP Server

The io.github.pipeworx-io/dc-dmv Model Context Protocol (MCP) server provides access to Washington DC DMV information, including service centers, vehicle inspections by VIN, and traffic convictions. It is identified by the slug io-github-pipeworx-io-dc-dmv and categorized under topics such as mcp, model-context-protocol, and dc-dmv.

🛠️ Key Features

Service centers (Washington DC DMV)
Vehicle inspections by VIN
Traffic convictions

🚀 Use Cases

Find relevant DMV service center information
Look up vehicle inspection details using a VIN
Review or reference traffic conviction information

⚡ Developer Benefits

Aligned with Model Context Protocol (mcp, mcp-server, model-context-protocol)
Domain-scoped to Washington DC DMV (dc-dmv, pipeworx)

⚠️ Limitations

Available documentation details are limited to the provided description and topics (no tool count or readme excerpt provided).

@pipeworx/dc-dmv

District of Columbia DMV data: service centers, the record-level vehicle inspection file, and traffic convictions. Keyless.

Why DC gets its own pack

DC's grain is unlike any state's. The District publishes 766,879 individual vehicle inspections keyed by VIN and 330,728 individual traffic convictions keyed by citation, both as ArcGIS tables on the DCGIS server — not the county-level aggregates most states publish. So the tools take VINs, violation text and date ranges rather than a county argument.

Tools

Tool	What it returns
`dc_dmv_offices`	All 9 DMV locations — service centers, the inspection station, two self-service emissions kiosks, the Deanwood road-test/CDL center, and Adjudication Services
`dc_dmv_vehicle_inspections`	Inspection records by VIN or VIN prefix, with inspection and expiration dates; or inspection volume per month/year
`dc_dmv_convictions`	Traffic convictions counted by violation or court disposition, or individual citation records

Auth

None. Every upstream is a public DCGIS ArcGIS MapServer layer.

The State Plane coordinate trap

The office layer's XCOORD/YCOORD columns are Maryland State Plane metres, not lat/lng. Georgetown Service Center reads 394380.55, 137456.23 — plausible-looking numbers that are wrong by the width of a hemisphere if treated as coordinates.

Every query here passes geometry: true to arcgisQuery (which sets outSR=4326) and reads latitude and longitude off the returned geometry. XCOORD/YCOORD are deliberately left out of the response so they cannot be mistaken for coordinates downstream. Georgetown's real position is 38.9050, -77.0648.

Gotchas worth knowing

These are live-verified behaviours, not guesses:

The inspection file's STATUS column is empty on all 766,879 rows, so a pass/fail outcome is not published. What you get is that an inspection happened, when, and when the resulting certificate expires — an expiration roughly two years out is the usual sign of a pass, a short one of a re-inspection window. The tool's note says so rather than letting a caller read a null as "unknown result". For per-test pass/fail with make and model, nj_mvc_vehicle_inspections publishes it.
GIS_ID, CREATOR and CREATED are also empty on the inspection table.
About half the conviction rows prefix the violation text with a four-digit internal code — 0201SPEED IN EXCESS OF LIMIT and SPEED IN EXCESS OF LIMIT are the same offence in two buckets. A raw group-by therefore splits and mis-ranks every violation. The tool strips the prefix and merges: "DISOBEYING OFFICIAL SIGN OR SIGNAL DEVICE" is 20,113 convictions, not the 10,369 a naive query reports.
DCGIS caps any single response at 2,000 rows, grouped statistics included. Month and year rollups group by day upstream (1,874 distinct days across the whole file — inside the cap) and aggregate in code; if that ever hits the cap the response flags truncated and says to narrow with inspected_since.
Coverage: inspections run 2020-11-01 → 2026-06-30 and are still being appended; convictions run 2002-07-01 → 2026-06-27. Both are returned in a coverage field rather than assumed.
Convictions are convictions. A citation that was dismissed or never adjudicated is absent, so these counts are not ticket counts.
The office layer carries no ZIP and no hours. MATCHADDR is the District's own geocoded street address; dmv.dc.gov posts hours per location.

Available but not wrapped

The same Transportation_DMV MapServer carries further DMV tables that are queryable but not exposed as tools:

Layer	Table
142	DMV Business Information
143	DMV Driver License Information
144	DMV ID Card Information
145	DMV Reciprocity Parking Permit
146	DMV Personalized Plates
147	DMV Disability Placard
149	DMV Vehicle Inspections FY2003-2013
150	DMV Vehicle Inspections FY2014-2020
166	DMV Boot and Tow

Layers 149 and 150 are the historical inspection archives and share the current table's schema, including the same empty STATUS.

Data sources

DCGIS Public_Service layer 4 — District agency locations, filtered to the 9 DMV rows
DCGIS Transportation_DMV layer 1 — DMV Vehicle Inspections
DCGIS Transportation_DMV layer 151 — DMV Convictions
Dataset discovery: opendata.dc.gov

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

json

{
  "mcpServers": {
    "dc-dmv": {
      "url": "https://gateway.pipeworx.io/dc-dmv/mcp"
    }
  }
}

Or connect to the full Pipeworx gateway for access to all 1392+ data sources:

json

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English:

code

ask_pipeworx({ question: "your question about District of Columbia DMV data" })

The gateway picks the right tool and fills the arguments automatically.

License

MIT

question	string	required	Your question or request in natural language. Accepts query, q, prompt, text, input as aliases.
query	string	optional	Alias for question.
q	string	optional	Alias for question.
prompt	string	optional	Alias for question.
text	string	optional	Alias for question.
input	string	optional	Alias for question.

question	string	required	Your question or request in natural language. Accepts query, q, prompt, text, input as aliases.
query	string	optional	Alias for question.
q	string	optional	Alias for question.
prompt	string	optional	Alias for question.
text	string	optional	Alias for question.
input	string	optional	Alias for question.

question	string	required	Your question in natural language. Accepts query, q, prompt, text, input as aliases.
query	string	optional	Alias for question.
q	string	optional	Alias for question.
prompt	string	optional	Alias for question.
text	string	optional	Alias for question.
input	string	optional	Alias for question.

text	string	required	The document text to search inside (max ~200K chars).
query	string	required	Natural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".
limit	number	optional	Max passages to return (1-20, default 5).

question	string	required	The research question, in natural language. Broad/multi-part is fine — decomposition is the point.
depth	string	optional	How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).

type	string	required	Entity type: "company" or "drug".
value	string	required	For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin").

claim	string	required	Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pct	number	optional	Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

package	string	required	npm package name. Scoped packages (e.g. "@types/node") are accepted.
version	string	optional	Specific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

market	string	required	Polymarket slug ("will-bitcoin-hit-150k-by-june-30-2026"), full URL ("https://polymarket.com/event/..."), or question text ("Will Bitcoin hit $150k by June 30?")
depth	string	optional	quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.
include_raw	boolean	optional	Default false. When false (recommended), FRED/FDA/GDELT/Federal-Register evidence is summarized to the few fields agents actually use — keeps responses under ~20KB. Pass true to get full upstream payloads (50KB-500KB) when you need to recompute deltas, cite specific observations, or post-process.

event	string	optional	Single-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
topic	string	optional	Cross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Polymarket for related events and checks monotonicity across them.

window	string	optional	Polymarket volume window to filter markets. Default 1wk.
limit	number	optional	Top N edges to return after ranking. Default 10, max 25.
min_edge_pp	number	optional	Minimum \|edge\| in percentage points to include (default 0.5). Edge is evaluated NET of slippage.
slippage_pp	number	optional	Assumed execution slippage in percentage points per leg (default 0.3). Subtracted from raw \|edge\| before ranking and Kelly sizing. Polymarket has zero trading fees as of 2024 but bid/ask + thin depth typically eats 20-50bp per trade. Bump for very thin partitions; drop to 0 if you have a smarter fill model.
category_filter	string	optional	Comma-separated list to restrict the output: "model_driven" (crypto_price + news_momentum), "structural_arbitrage" (partition_overround), "concentrated_longshot". Combine like "model_driven,structural_arbitrage". Default: all.
min_kelly	number	optional	Minimum half-Kelly fraction (as decimal, e.g. 0.005 = 0.5% of bankroll) to include single-leg opportunities. Default 0 (no filter). Skips opportunities that are too small to bet sensibly even if the edge is large.
min_partition_leg_kelly	number	optional	Minimum BEST per-leg half-Kelly fraction across a partition_overround opportunity's top_legs (or longshot_basket legs). Default 0 (no filter). Partition arbs always return kelly_fraction_half=0 at the parent level by design (basket trades don't compose to single-leg Kelly), so min_kelly never filters them — this knob applies to the per-leg Kelly inside top_legs instead. Use to suppress thin partitions whose individual leg edges aren't worth the per-leg slippage cost.
min_liquidity	number	optional	Tradeable-edge filter. Minimum $ liquidity on the representative market (or for partition_overround, on at least one top_leg). Default 0 (no filter). Set to 5000 to drop thin-book opportunities where executing the edge would walk the book past breakeven.
max_spread_pp	number	optional	Tradeable-edge filter. Maximum bid/ask spread in percentage points on the representative market. Default null (no filter). Set to 2 to require tight books — anything wider eats most plausible edges.

days	number	optional	Lookback in days (default 14, clamp 2-30).
window	string	optional	Which polymarket_edges window family to read snapshots for: 24hr \| 1wk \| 1mo (default 1wk).

url	string	required	Full URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_links	number	optional	Maximum number of link entries to include (default 25, max 50).

entity	string	required	The thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
context	string	optional	Optional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.
models	array	optional	Which models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKey	string	optional	Optional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.

entities	array	required	Array of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.
context	string	optional	Optional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
models	array	optional	Which models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKey	string	optional	Optional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.

type	string	required	Subscription type.
params	object	required	Type-specific filter. sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}. polymarket_edge: {topic:"fed", min_spread_bps?:500}. fred_series: {series_id:"UNRATE"}. patent_grant: {applicant:"Apple Inc."}. clinical_trial: {sponsor?:"Pfizer", condition?:"lung cancer", phase?:"PHASE3"} (sponsor or condition required).
delivery	object	optional	Optional delivery channels in addition to the always-on persistent feed. {email:"you@x.com"} sends a templated alert per fired event. {sms:"+15551234567"} sends an SMS per event — must match the verified phone on the caller's account (verify at https://pipeworx.io/account first; 10/day cap). {webhook:"https://..."} POSTs each event JSON to your endpoint, HMAC-signed — the response includes delivery.webhook_secret (whsec_…) ONCE; verify X-Pipeworx-Signature = sha256 HMAC of "<X-Pipeworx-Timestamp>.<raw body>". Auto-disabled after 10 consecutive failing runs.

type	string	required	Entity type. Only "company" supported today.
value	string	required	Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").
since	string	required	Window start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.

topic	string	optional	Pre-mapped: fed \| btc \| cpi \| gdp \| sp500 \| recession \| next_pope \| next_uk_pm \| next_israel_pm \| 2028_president
kalshi_event_ticker	string	optional	Explicit Kalshi event ticker, e.g. "KXFED-26OCT". Overrides the topic-mapped Kalshi side.
polymarket_event_slug	string	optional	Explicit Polymarket event slug, e.g. "fed-decision-in-june-825". Overrides the topic-mapped Polymarket side.

market	string	optional	Single-market mode: market slug or full polymarket.com URL.
event	string	optional	Basket mode: event slug or full polymarket.com URL — checks every leg of the partition.
side	string	optional	Single-market: buy_yes \| sell_yes \| buy_no \| sell_no (default buy_yes). Basket: sell_yes \| buy_yes (default auto — sell if partition sum > 1, buy if < 1).
size_usd	number	optional	Single-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

key	string	required	Memory key (e.g., "subject_property", "target_ticker", "user_preference")
value	string	required	Value to store (any text — findings, addresses, preferences, notes)

name	string	optional	Location-name substring, e.g. "Georgetown", "Benning", "Inspection", "Adjudication".
office_type	string	optional	Category substring: "service center", "inspection station", "kiosk", "road test", "adjudication".
limit	number \| string	optional	Max locations to return (default 25, max 50).

vin	string	optional	Full 17-character VIN, e.g. "W1KAF4GB5SR257239". Case-insensitive.
vin_prefix	string	optional	Leading characters of a VIN, e.g. "1FMCU9BZ" — the first 8 identify make, model line and body, so this counts one model.
inspected_since	string	optional	Earliest inspection date, YYYY-MM-DD, e.g. "2026-01-01".
inspected_before	string	optional	Latest inspection date, YYYY-MM-DD, e.g. "2026-07-01".
expires_before	string	optional	Keep inspections expiring before this date, YYYY-MM-DD — the way to find vehicles due for re-inspection.
group_by	string	optional	none (default) returns individual inspection records; month or year returns inspection counts per period.
limit	number \| string	optional	Max records or periods to return (default 25, max 200).

violation	string	optional	Violation-text substring, e.g. "SPEED", "SEAT BELT", "INFLUENCE", "INSURANCE".
code	string	optional	DC violation code, e.g. "T001", "T005".
disposition	string	optional	Court-disposition substring, e.g. "GUILTY", "LIABLE", "SUPERIOR".
since	string	optional	Earliest citation date, YYYY-MM-DD, e.g. "2025-01-01".
before	string	optional	Latest citation date, YYYY-MM-DD, e.g. "2026-01-01".
group_by	string	optional	violation (default) counts convictions per violation; disposition counts them per court outcome; none returns individual citation records.
limit	number \| string	optional	Max rows to return (default 25, max 200).

io.github.pipeworx-io/dc-dmv MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

ask_pipeworx

ask_pipeworx_beta

ask_pipeworx_grounded

search_within

deep_research

discover_tools

resolve_entity

compare_entities

subscribe

unsubscribe

list_subscriptions

recent_alerts

entity_profile

recent_changes

validate_claim

scan_dependency

bet_research

polymarket_arbitrage

polymarket_edges

polymarket_kalshi_spread

polymarket_fill_risk

polymarket_edge_tracker

pipeworx_trending

suggest_questions

generate_llms_txt

ai_visibility_check

scan_competitor_ai_presence

pipeworx_feedback

remember

recall

forget

dc_dmv_offices

dc_dmv_vehicle_inspections

dc_dmv_convictions