Server data from the Official MCP Registry
Vehicle specs, federal recalls, and OBD-II DTC reference for AI agents via MCP.
Vehicle specs, federal recalls, and OBD-II DTC reference for AI agents via MCP.
Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
3 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: CARVECTOR_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-carvectorio-carvector-mcp": {
"env": {
"CARVECTOR_API_KEY": "your-carvector-api-key-here"
},
"args": [
"-y",
"carvector-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
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
· MIT · Free tier, no card → carvector.io
1. Get a free API key at carvector.io — 100 requests/day, 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" } } } }
| 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.
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.
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:
Calls count against your plan's rate limit and show up in your dashboard, exactly like a REST request.
This client is ~150 lines of readable JavaScript — please read them. It:
api.carvector.io (grep index.js, it's the only URL),Bearer header to that host, nowhere else,@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.
MIT. The client is open source; the data is served by CarVector.
Be the first to review this server!
by Modelcontextprotocol · AI & ML
Dynamic and reflective problem-solving through structured thought sequences
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
by mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.