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, 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
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Get a free API key at carvector.io — 100 requests/day, no credit card.
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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 Tool What it returns search_vehicles Matching vehicles by year / make / model, with ids + specs get_vehicle Full specs for one vehicle — engine, drivetrain, body, image, recall count get_recalls Federal recall campaigns for a vehicle — component, summary, consequence, remedy lookup_dtc An OBD-II code's title, category, severity, and safety/emissions flags The agent chains them naturally: search_vehicles to resolve an id, then get_vehicle / get_recalls.
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, 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. 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.
Server Config
{
"mcpServers": {
"carvector": {
"command": "npx",
"args": [
"-y",
"carvector-mcp"
],
"env": {
"CARVECTOR_API_KEY": "cv_your_key"
}
}
}
}