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mcp-strava

mcp-strava

by lyzetam
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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    • strava-mcp

      naoiseirwin
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables GrokBot users to read their own Strava data from a self-hosted stdio server, querying activities with filters, laps, zones, and streams, viewing athlete profiles and stats, exploring segments and leaderboards, listing routes, exporting GPX/TCX, and analyzing or comparing training. It is read-only, using OAuth tokens stored locally on the user's cloud computer with scopes limited to profile and activity reads.
      MIT

    • Strava MCP Server

      juanlarreapm
      A
      license
      B
      quality
      D
      maintenance
      Integrates with the Strava API to allow AI assistants to access fitness data including athlete profiles, activity history, and segment statistics. It enables users to query detailed performance metrics and explore geographic segment data through natural language commands.
      8
      95 npm
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool targets a clearly distinct resource and action: athlete summary vs stats vs zones, activity list vs detail vs laps vs zones vs streams, and segment starred list vs detail vs efforts. The descriptions reinforce the boundaries, so an agent can reliably choose the right tool.

    Naming Consistency5/5

    All tools use consistent snake_case with predictable get_/list_ verb prefixes followed by the resource name. Nested concepts such as get_activity_zones and list_segment_efforts remain readable and follow the same convention.

    Tool Count5/5

    The 14 tools are well-scoped for a Strava read/analytics server. Each tool covers a meaningful slice of the domain without excessive surface area.

    Completeness4/5

    The surface covers the main read workflows: athlete profile, stats, zones, activities, streams, segments, routes, gear, and clubs. Minor gaps remain, such as no get_route or get_club detail after list_routes/list_clubs, and no activity upload or write operations, but agents can still handle most exploration tasks.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources