What does this server do?
It connects Strava’s training data to MCP tools for analyzing activities, calculating training load, and offering recommendations.
@rbctmz
Basic information
Интеграция Strava API с Model Context Protocol SDK
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-strava": {
"command": "uv",
"args": [
"pip",
"install",
"."
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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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