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Fastf1 Mcp Server

@Surya96t

代码仓库

关于 Fastf1 Mcp Server

MCP server for Formula 1 data via the FastF1 library. Ask Claude (or any MCP-compatible client) about race results, lap times, telemetry, standings, pit stops, and qualifying — with historical data back to 1950 via the Ergast API.

#formula1#f1#fastf1#telemetry#motorsport#mcp#sports#data#ergast#claude

基本信息

分类

开发工具

传输方式

stdio

发布者

Surya96t

提交者

Surya Prakash

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "fastf1": {
      "command": "uv",
      "args": [
        "run",
        "fastf1-mcp-server"
      ],
      "cwd": "/absolute/path/to/fastf1-mcp",
      "env": {
        "FASTF1_MCP_LOG_LEVEL": "INFO",
        "FASTF1_MCP_MAX_CACHED_SESSIONS": "10"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Fastf1 Mcp Server?

Fastf1 Mcp Server is an MCP server that exposes Formula 1 data to AI assistants via the FastF1 library. It provides 21 tools, 4 resources, and 5 prompts for querying race results, lap times, telemetry, standings, and more.

How to use Fastf1 Mcp Server?

Install with uv or pip, configure via environment variables, then run the server directly or add it to your MCP client configuration (e.g., Claude Desktop's claude_desktop_config.json). The server runs locally and communicates over stdio.

Key features of Fastf1 Mcp Server

  • 21 tools covering standings, results, telemetry, and strategy.
  • 4 MCP resources for schedule, driver, constructor, and circuit data.
  • 5 guided prompts for race recaps and analysis.
  • Async-safe LRU session cache for fast repeat queries.
  • Automatic telemetry sampling to ≤ 500 data points.

Use cases of Fastf1 Mcp Server

  • Ask an AI assistant for a full race recap including strategy.
  • Compare telemetry between two drivers in a qualifying session.
  • Analyze who had the fastest theoretical lap in a session.
  • Look up constructor or driver standings at any point in a season.

FAQ from Fastf1 Mcp Server

What data sources does the server use?

It uses the Ergast API (1950–present) for historical data and FastF1 Live Timing (2018–present) for session data.

What are the runtime requirements?

Python 3.12+ and either uv (recommended) or pip for installation.

How does telemetry sampling work?

Telemetry data is sampled to a maximum of 500 points (default 200) to manage large raw datasets.

How is caching handled?

An LRU in-memory cache stores up to 10 sessions by default, configurable via FASTF1_MCP_MAX_CACHED_SESSIONS. A disk cache path is also configurable.

What authentication or transport is required?

No authentication is needed. The server runs locally via stdio transport and can be tested with the MCP Inspector.

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