What software is required?
Python 3.10+, Claude Desktop, a Strava account, and the `uv` package manager (recommended).
@MCP-Mirror
Basic information
Mirror of
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"rbctmz_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.
An MCP server that connects Strava data to AI assistants via the Model Context Protocol. It analyzes workouts, provides training recommendations, and manages Strava API authentication automatically.
Install Python 3.10+, clone the repository, and use uv to install dependencies. Configure a Strava API application, create a .env file from the template, run python scripts/auth.py to obtain tokens, then launch with mcp dev src/server.py.
Python 3.10+, Claude Desktop, a Strava account, and the `uv` package manager (recommended).
Run `python scripts/auth.py` after setting up your Strava API application and filling the required client ID, client secret, and callback domain in the `.env` file.
Yes, the server enforces a rate limit of 100 API requests per 15 minutes to comply with Strava’s limits.
Resources: `strava://activities`, `strava://activities/{id}`, `strava://athlete/zones`, `strava://athlete/clubs`, `strava://gear/{gear_id}`. Tools: `analyze_activity`, `analyze_training_load`, `get_activity_recommendations`.
Yes, it is released under the MIT license and contributions are welcome via GitHub pull requests.
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