mcp-f1analisys
Enables Formula 1 data analysis through natural language, providing tools like track dominance, lap time analysis, and team performance comparisons.
README
🏎️ MCP Server F1Analisys
<img src="./content/example.gif" width="1000">
A Model Context Protocol (MCP) server for interacting with F1Analisys through LLM interfaces like Claude. You will need to have Claude installed on your system to continue.
Instalation
First of all, you need to install mcp-f1analisys package from pypi with pip, using the following command:
pip install mcp-f1analisys
To use mcp-f1analisys server in claude can be configured by adding the following to your configuration file.
- Windows:
%APPDATA%/Claude/claude_desktop_config.json - Linux:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the F1Analisys MCP server configuration:
{
"mcpServers": {
"mcp-f1analisys": {
"command": "python",
"args": [ "-m", "mcp-f1analisys" ]
}
}
}
Tools
- Track dominance
- Top speed
- Lap time average
- Lap time distribution
- Team performance
- Fastest laps
- Race position evolution
- Fatest drivers each compound
- Comparative lap time
- Throttle usage
- Braking usage
- Long runs
- Optimal lap impact
Launch
Active the virtual environment and install the requirements using:
.\.venv\Scripts\activate
Install the mcp server in Claude using the following command:
mcp install .\server.py
Requirements
The requirementes used to build this MCP server are:
mcp[cli]httpxfastmcp
Testing
You can test the server using the MCP Inspector:
mcp dev .\server.py
License
Source Code
The source code of this project is licensed under the Apache License 2.0.
Data
This project uses Formula 1 data from FastF1 created by the FastF1 contributors, licensed under the MIT License license.
Notice
MCPF1Analisys is unofficial and are not associated in any way with the Formula 1 companies. F1, FORMULA ONE, FORMULA 1, FIA FORMULA ONE WORLD CHAMPIONSHIP, GRAND PRIX and related marks are trade marks of Formula One Licensing B.V.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.