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Vincario Vehicle Data

Vincario Vehicle Data

Remote
github.com/VincarioData & Analytics2Updated 29w ago
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About Vincario Vehicle Data

Vincario Vehicle Data is an MCP server for AI clients that need vehicle information from the Vincario API. It provides VIN decoding, stolen-vehicle checks, and market valuations through tools over streamable HTTP.

Key features

  1. 01Detailed VIN decoding
  2. 02List available VIN fields
  3. 03Check stolen vehicle databases
  4. 04Estimate vehicle market value
  5. 05Accept odometer input for valuations

Use cases

  1. 01Retrieve specifications for a vehicle from its VIN
  2. 02Check whether a vehicle is listed as stolen
  3. 03Estimate a vehicle’s market value with mileage

MCP support

Exposes Vincario API operations as tools for MCP clients, using streamable HTTP transport. Requests require a Vincario API key in the X-API-Key header.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.vincario.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

4
ParameterTypeRequiredDescription
vinstringYes

Details

From the project README

Vincario MCP Server

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.

Tools

ToolDescription
vin_decodeDecode a VIN and return detailed vehicle information
vin_decode_infoList available fields for a given VIN (free endpoint)
stolen_checkCheck if a VIN appears in stolen vehicle databases
vehicle_market_valueGet market valuation for a vehicle (supports odometer input)

Requirements

  • Vincario API key — passed via X-API-Key HTTP header
  • Docker (recommended), or Python 3.11+ with uv

Running with Docker

docker build -t vincario-mcp .
docker run -p 8080:8080 vincario-mcp

The server starts on http://localhost:8080.

Running locally

pip install uv
uv sync
uv run main.py

Connecting to an MCP client

Pass your Vincario API key as an HTTP header with each request:

X-API-Key: your_api_key_here
Claude Code

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"
      }
    }
  }
}
Hosted endpoint

If connecting to the hosted server at https://mcp.vincario.com/mcp, replace the URL accordingly.

Example usage

Once connected, you can ask your AI assistant:

  • "Decode VIN WBAFR9C54BC780006"
  • "Is this vehicle stolen? VIN: WBAFR9C54BC780006"
  • "What is the market value of VIN WBAFR9C54BC780006 with 85000 km?"

Transport

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.

License

See Vincario API Terms of Service for usage terms.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.