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  • F
    license
    Not graded
    quality
    D
    maintenance
    Exposes Garmin Connect health and activity data (steps, sleep, heart rate, etc.) via MCP tools, with built-in login and MFA support.
    -
  • A
    license
    A
    quality
    A
    maintenance
    Provides local MCP tools to query your own Xiaomi Mi Fitness health data (sleep, workouts, heart rate, etc.) stored in a local SQLite database, enabling AI assistants to access personal health records without exposing credentials.
    17
    34
    AGPL 3.0
  • A
    license
    A
    quality
    A
    maintenance
    Ask your Oura Ring about sleep, readiness, activity, stress and heart rate in ChatGPT or Claude, in any language. Read-only, 10 task-oriented tools; runs in a sandbox demo mode with no credentials.
    10
    32 npm
    1
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Query Apple Health export data with an LLM via a semantic layer that avoids common data traps, enabling accurate natural-language queries about health metrics. Optionally imports CPAP therapy data for correlation.
    3
    MIT
  • F
    license
    A
    quality
    D
    maintenance
    A stateless MCP server that normalizes strength training logs into a deterministic canonical schema, enabling consumers to build analytics downstream without storing data server-side.
    3
    -
  • A
    license
    A
    quality
    A
    maintenance
    This MCP server enables logging workouts in plain language through AI assistants and provides tracking of sessions, programs, records, estimated 1RM, and per-muscle load, with coaching informed by the user's logged data.
    20
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Enables an LLM to search a movement library, log single-movement and multi-movement workout results, and pull complete logged training history plus PR trends for Beyond the Whiteboard straight from a chat. All logged entries are posted privately, and session authentication can refresh automatically.
    7
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    A local-first, read-only MCP server that provides compact recovery, sleep, strain, HRV, heart-rate, workout, and body-measurement data from WHOOP without sending credentials to a third party.
    5
    77 npm
    1
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Enables agents to read Mi Band health records from a computer, providing recent status, daily reports, historical details, and data sync via Gadgetbridge and USB, with agent-guided setup.
    11
    19
    AGPL 3.0
  • F
    license
    A
    quality
    B
    maintenance
    Provides a unified MCP interface to a personal health database, enabling AI agents to sync lifting workouts from Hevy, log food intake against a catalog, and run read-only SQL queries to cross-reference training and nutrition data.
    6
    1
    -
  • A
    license
    A
    quality
    B
    maintenance
    Enables AI assistants to access and analyze Polar device health data, including exercises, heart rate, sleep, and recovery metrics, by preprocessing raw sensor data into compact summaries to avoid context window overload.
    13
    MIT
  • A
    license
    A
    quality
    F
    maintenance
    Aggregates and analyzes fitness data from multiple sources like Whoop and Strava through a modular adapter architecture. It enables users to monitor health metrics, track activities, and gain insights into sleep, recovery, and training performance.
    1
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Enables AI agents to sync WHOOP recovery, sleep, workouts, cycles, and body-measurement data into a local Postgres database and query it through fast, offline-capable MCP tools without repeatedly hitting WHOOP API rate limits.
    10
    16 PyPI
    MIT
  • A
    license
    A
    quality
    F
    maintenance
    An MCP server that provides access to detailed exercise-to-muscle mapping data for over 320 exercises and 65 muscles. It enables users to analyze workout coverage, find exercise alternatives based on muscle overlap, and search for specific exercise activation data.
    6
    25 npm
    4
    MIT