Overview
What is Formula One MCP Server?
A Model Context Protocol (MCP) server that provides Formula One racing data via tools for querying event schedules, driver information, telemetry, race results, and championship standings. It is built on the FastF1 Python package and is designed for developers integrating F1 data into AI assistants or data analysis applications.
How to use Formula One MCP Server?
Install the package with pip install f1-mcp-server or uv add f1-mcp-server in a Python project. Run the server in standard I/O mode with uvx f1-mcp-server or in SSE transport mode (e.g., for web applications) with uvx f1-mcp-server --transport sse --port 8000. The server can also be invoked via the Python API with main().
Key features of Formula One MCP Server
- Access complete F1 race calendar for any season
- Get detailed information about specific Grand Prix events
- Retrieve results from races, qualifying, sprints, and practice
- Access driver details for specific sessions
- Analyze a driver’s performance with lap time statistics
- Compare multiple drivers’ performances in the same session
Use cases of Formula One MCP Server
- Query upcoming F1 race schedules in an AI assistant
- Compare teammates’ lap times in a qualifying session
- Analyze a driver’s performance trends across multiple races
- Retrieve championship standings for a given season
- Obtain telemetry data for a specific lap to analyze driving technique
FAQ from Formula One MCP Server
What data does the server provide?
It exposes tools for event schedules, detailed Grand Prix info, session results, driver information, performance analysis, driver comparisons, telemetry data, and championship standings (both drivers and constructors).
How do I install the server?
Install via pip install f1-mcp-server or add f1-mcp-server to a uv project with uv add f1-mcp-server.
How do I run the server?
Use uvx f1-mcp-server to run in an isolated environment, or uv run f1-mcp-server within a project. The server supports standard I/O (default) and SSE transport modes for web applications.
What are the main dependencies?
Dependencies include anyio, click, fastf1, mcp, numpy, pandas, and uvicorn.
Is the server open source?
Yes, it is licensed under the MIT License and hosted on GitHub.
