Vehicle data for AI: VIN decoder, automotive specs, stolen checks, valuation and way more.
{
"mcpServers": {
"com-vincario-vehicle-data": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Vehicle data for AI: VIN decoder, automotive specs, stolen checks, valuation and way more.
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An MCP (Model Context Protocol) server that exposes the Vincario API to AI agents and LLM clients. Enables AI assistants to decode VINs, check stolen vehicle databases, and retrieve market valuations through natural language.
| Tool | Description |
|---|---|
vin_decode | Decode a VIN and return detailed vehicle information |
vin_decode_info | List available fields for a given VIN (free endpoint) |
stolen_check | Check if a VIN appears in stolen vehicle databases |
vehicle_market_value | Get market valuation for a vehicle (supports odometer input) |
X-API-Key HTTP headerdocker build -t vincario-mcp .
docker run -p 8080:8080 vincario-mcp
The server starts on http://localhost:8080.
pip install uv
uv sync
uv run main.py
Pass your Vincario API key as an HTTP header with each request:
X-API-Key: your_api_key_here
Add to your MCP config (.mcp.json or claude_desktop_config.json):
{
"mcpServers": {
"vincario": {
"type": "http",
"url": "http://localhost:8080",
"headers": {
"X-API-Key": "your_api_key_here"
}
}
}
}
If connecting to the hosted server at https://mcp.vincario.com/mcp, replace the URL accordingly.
Once connected, you can ask your AI assistant:
The server uses streamable HTTP transport (stateless_http=True), which means no persistent session is required. Each request is independent, making it straightforward to deploy behind a reverse proxy or load balancer.
For HTTPS deployment, place a reverse proxy (nginx, Caddy, Cloudflare) in front of the server — the application itself runs on plain HTTP port 8080.
See Vincario API Terms of Service for usage terms.