What does this server do?
It connects Strava’s training data to MCP tools for analyzing activities, calculating training load, and offering recommendations.
@rbctmz
基本信息
Интеграция Strava API с Model Context Protocol SDK
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-strava": {
"command": "uv",
"args": [
"pip",
"install",
"."
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
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。
This server integrates the Strava API with the Model Context Protocol (MCP) SDK, enabling AI assistants to analyze workouts and provide training recommendations based on the user’s Strava data. It is intended for athletes and developers who want to leverage Strava activity data within MCP‑compatible clients such as Claude Desktop.
Clone the repository, install dependencies with uv, create a Strava API application, configure environment variables (.env), run python scripts/auth.py to obtain tokens, and then start the server with mcp dev src/server.py. Resources are accessed via URIs like strava://activities and tools such as analyze_activity(activity_id).
It connects Strava’s training data to MCP tools for analyzing activities, calculating training load, and offering recommendations.
Python 3.10+, [Claude Desktop](https://claude.ai/desktop), a Strava account, and [uv](https://github.com/astral-sh/uv) (recommended).
Create a Strava API application, set `STRAVA_CLIENT_ID` and `STRAVA_CLIENT_SECRET` in your `.env` file, then run `python scripts/auth.py` to obtain and store the refresh token.
The server enforces a limit of 100 requests per 15 minutes.
Tokens are stored in a local `.env` file, which should be added to `.gitignore`. For CI/CD, secrets can be set via GitHub Secrets.
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