CarVector

Give your AI agent real vehicle data. An MCP server that lets Claude, Cursor, ChatGPT, or any MCP-capable client query the CarVector API natively — vehicle specs, representative images, federal recalls, owner complaints, service bulletins, defect investigations, and OBD-II diagnostic trouble codes.

Models hallucinate car data. They invent horsepower numbers, miss recalls filed last week, and guess at what a trouble code means. carvector-mcp gives your agent structured, sourced answers it can cite instead of a confident guess.

npx -y carvector-mcp --key cv_your_key

npm · MIT · Free tier, no card → carvector.io

Quickstart

1. Get a free API key at carvector.io — 500 requests a month, no credit card.

2. Add it to your MCP client. Most clients use an mcpServers block:

{
  "mcpServers": {
    "carvector": {
      "command": "npx",
      "args": ["-y", "carvector-mcp"],
      "env": { "CARVECTOR_API_KEY": "cv_your_key" }
    }
  }
}

That's it. Restart your client and ask it about a vehicle.

Prefer a remote server? If your client supports HTTP MCP, skip the install and point it straight at the hosted endpoint:

{ "mcpServers": { "carvector": {
    "url": "https://api.carvector.io/v1/mcp",
    "headers": { "Authorization": "Bearer cv_your_key" } } } }

Tools

The agent chains them naturally: search_vehicles to resolve an id, then get_vehicle, get_recalls, get_complaints, get_tsbs, or get_investigations.

Example

You: "Is a P0300 code serious?"

→ carvector.lookup_dtc({ code: "P0300" })
{
  "code": "P0300",
  "title": "Random/Multiple Cylinder Misfire Detected",
  "category": "Powertrain",
  "severity": "High",
  "safety_risk": true,
  "emissions_related": true
}

Your agent answers: "Yes — P0300 is a high-severity, safety-related misfire code. Don't keep driving on it." Sourced, not guessed.

Three things to build with it

• A service-advisor copilot that pulls a customer's exact trim, open recalls, the manufacturer's documented fix (TSBs), the complaint pattern behind a symptom, and a decoded check-engine code — in one turn, no tab-switching.
• A consumer car chatbot that answers "what engine does my truck have" and "is it under recall" with real data instead of a hallucination.
• A coding/automotive agent that needs structured vehicle knowledge as a tool, not a wall of scraped text to parse.

About the data

carvector-mcp is an open-source, thin client. It bundles no data — every call forwards to the CarVector API, authenticated with your key. What you get back:

• Vehicles — a broad catalog (1925–2029), broken out by trim and engine variant, with representative illustrations (not photos).
• Recalls — federal recall campaigns mapped to year / make / model.
• Complaints — owner-filed complaints aggregated by component (with crash / fire / injury counts) plus the most recent filings, mapped to a vehicle. (Pro plan.)
• Service bulletins (TSBs) — the manufacturer's technical service-bulletin index — metadata, not the documents. (Business plan.)
• Investigations — federal defect investigations, a leading indicator that often precedes a recall. (Business plan.)
• DTC reference — OBD-II codes classified by category, severity, and safety/emissions flags. Reference only — repair-cost economics is on the roadmap, not in responses today.

Calls count against your plan's rate limit and show up in your dashboard, exactly like a REST request.

Open source & your key

This client is ~150 lines of readable JavaScript — please read them. It:

• talks to one host only — api.carvector.io (grep index.js, it's the only URL),
• sends your key only as a Bearer header to that host, nowhere else,
• has zero telemetry, analytics, or phone-home, and writes nothing to disk,
• depends on exactly one package: the official @modelcontextprotocol/sdk.

Your key stays on your machine. Set it via the CARVECTOR_API_KEY env var (preferred); --key works too but, like any CLI argument, is visible in process listings.

License

MIT. The client is open source; the data is served by CarVector.