Soccer MCP Server

@MCP-Mirror

代码仓库

基本信息

分类

其他

传输方式

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

What API key is required?

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

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.

What are the technical dependencies?

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

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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