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Soccer MCP Server

@MCP-Mirror

基本信息

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其他

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stdio

作者

MCP-Mirror

关于 Soccer MCP Server

Mirror of

#soccer#mcp-server#football-data

Soccer MCP Server 配置

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该服务器的 README 中没有可解析的 MCP 配置块,请前往代码仓库查看安装说明。

代码仓库

Soccer MCP Server 工具

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概览

What is Soccer MCP Server?

A Python server implementing the Model Context Protocol (MCP) for football (soccer) statistics and live match data. It acts as a bridge between applications and the API-Football service via RapidAPI, providing tools for leagues, teams, players, and live match information.

How to use Soccer MCP Server?

Obtain a RapidAPI key for the API-Football service and set it as the environment variable RAPID_API_KEY_FOOTBALL. Run the server with python soccer_server.py or mcp run soccer-server.py. Integrate with Claude Desktop using either Docker or direct Python execution by configuring claude_desktop_config.json.

Key features of Soccer MCP Server

  • Retrieve league standings, fixtures, and schedules
  • Search and fetch team information and fixtures
  • Look up player profiles and detailed statistics
  • Access live match data for a specific team
  • Get in-game events (goals, cards, substitutions)
  • Fetch fixture statistics for past or upcoming matches

Use cases of Soccer MCP Server

  • Get the current Premier League standings and upcoming fixtures
  • Find live match statistics and events for a favorite team
  • Compare player performance across seasons and leagues
  • Analyze historical match data for multiple fixtures at once
  • Retrieve team schedules within a custom date range

常见问题

1

What API key is required?

A RapidAPI key subscribed to the API-Football service, set as the environment variable `RAPID_API_KEY_FOOTBALL`.

2

How do I run the server?

Execute `python soccer_server.py` or `mcp run soccer-server.py` after installing dependencies and setting the API key.

3

What are the technical dependencies?

Python, the Fast MCP framework, Pydantic for input validation, and the Requests library for API communication.

4

How can I integrate this server with Claude Desktop?

Use Docker (build the image, run the container) or direct Python execution, then add the corresponding configuration to `claude_desktop_config.json` and restart Claude Desktop.

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