io.github.pipeworx-io/ct-dmv (MCP Server)

Connecticut DMV model-context server focused on vehicle-level electric vehicle registrations. It provides context data organized by town, vehicle make, and model, using the Model Context Protocol (MCP). The server’s catalog entry is identified by slug io-github-pipeworx-io-ct-dmv and is associated with MCP and Pipeworx.

🛠️ Key Features

Vehicle-level electric vehicle registrations
Filters and organization by town, make, and model
MCP server for model-context provisioning

🚀 Use Cases

Look up electric vehicle registration context in Connecticut by town
Use vehicle make and model to scope results for downstream tools

⚡ Developer Benefits

Structured context aligned to Model Context Protocol concepts
Topic coverage for MCP and MCP server discovery

⚠️ Limitations

Available metadata describes registrations by town, make, and model only; no additional tool or capability details are provided
No tool count or readme excerpt included in the source data

@pipeworx/ct-dmv

Connecticut DMV MCP — electric-vehicle registrations at vehicle level, aggregated on demand by town, make, model, model year or body type.

Tools

ct_dmv_ev_registrations(group_by?, city?, make?, model?, model_year?, vehicle_type?, limit?) — counts of registered electric vehicles. Answers "how many EVs are registered in Connecticut", "how many Teslas are registered in Connecticut", "which Connecticut town has the most EVs", "most popular EV models in Connecticut".

group_by accepts city (default), make, model, model_year, vehicle_type, body, fuel, color, plate_type, usage, category.

Auth

Keyless.

Vintage — read this before quoting a number

The file is a snapshot taken 2025-02-28. Connecticut has not refreshed it since, so it covers the electric vehicles registered as of February 2025 and a count read as "today" is wrong by more than a year. as_of carries the refresh date, covers states the period in words, and a note repeats it on every response.

Grain

One row per registered vehicle — 60,489 of them, each with plate type, owner town, weight, colour, model year and expiry. The pack never returns those individual records; it aggregates server-side with $select=<col>,count(1)&$group=<col> and returns counts.

sum_of_returned_rows is the sum of the rows actually returned, and truncated says whether limit cut the list off. There are ~170 Connecticut towns and hundreds of models, so a default call is usually truncated — which is why no field claims to be a statewide EV total.

Verified live 2026-07-29, group_by="make": Tesla 21,405 · Toyota 5,759 · Jeep 5,302 · Chevrolet 2,982 · KIA 2,916. Total rows in the file: 60,489.

Data sources

https://data.ct.gov/resource/y7ky-5wcz.json — CT DMV "Electric Vehicle Registration Data".
https://data.ct.gov/api/views/y7ky-5wcz.json — rowsUpdatedAt, used for as_of.

Column names are lowercase and unseparated: primarycustomercity, vehiclemake, vehiclemodel, vehicleyear, vehicletype, vehiclebody, fuelcode, platetype, registrationusage. Town names are stored upper-case (WEST HARTFORD) while makes are mixed case (Tesla, KIA), so both are matched with upper(col) like.

Rejected while building: apne-w8c6, published as "Licensed Automobile Dealers And Repairers", sounds like a dealer locator but holds 138 manufacturer licences, mostly held by out-of-state RV and trailer makers — 23 in Indiana, 18 in California, 2 in Connecticut, and every row is MANUFACTURER LICENSE. It cannot answer "find a licensed dealer or repairer near me", so no tool wraps it. Do not re-probe it expecting a dealer list.

Quick Start

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

json

{
  "mcpServers": {
    "ct-dmv": {
      "url": "https://gateway.pipeworx.io/ct-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 Connecticut 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)

group_by	string	optional	Breakdown dimension: city (default), make, model, model_year, vehicle_type, body, fuel, color, plate_type, usage, category.
city	string	optional	Connecticut town of the registered owner, e.g. "Stamford", "West Hartford".
make	string	optional	Vehicle make, matched as a substring, e.g. "Tesla", "Toyota", "Jeep".
model	string	optional	Vehicle model, matched as a substring, e.g. "Model 3", "Wrangler".
model_year	string	optional	Four-digit model year, e.g. "2024".
vehicle_type	string	optional	Vehicle type as Connecticut records it, matched as a substring, e.g. "Passenger", "Truck".
limit	number \| string	optional	Max rows to return (default 25, max 200).

io.github.pipeworx-io/ct-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

ct_dmv_ev_registrations