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Mi Fitness MCP CN

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    • Mi Fitness MCP

      aestilog
      A
      license
      C
      quality
      B
      maintenance
      Enables syncing Xiaomi Mi Fitness cloud health data into a local SQLite cache and querying it through MCP tools for daily activity, heart rate, sleep, workouts, body measurements, blood oxygen, stress, and abnormal heartbeat records.
      14
      MIT

    • Mi Fitness Data Bridge

      shkyyy18
      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
      51
      AGPL 3.0

    • mifit

      Jenwein
      A
      license
      Not graded
      quality
      A
      maintenance
      Lets AI assistants read your own Mi Fitness (Xiaomi cloud) health data — steps, heart rate, sleep, SpO2, stress, weight and workouts — through local MCP tools with QR-code login. Runs locally over stdio, returns results as JSON, and withholds GPS tracks unless explicitly allowed.
      385 PyPI
      MIT

    • Zepp Life MCP

      kubulashvili
      A
      license
      B
      quality
      C
      maintenance
      MCP server that provides local caching, sync, and tools for Zepp Life health data including steps, sleep, heart rate, workouts, and body measurements, supporting both file exports and cloud session access.
      10
      15
      MIT

    • Health Vault

      ilyanehay
      A
      license
      Not graded
      quality
      C
      maintenance
      Self-hosted MCP server that syncs Xiaomi fitness data to SQLite and provides authenticated tools to query health metrics (steps, sleep, HR, etc.) for AI assistants like Grok.
      GPL 3.0
    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

    C2.5/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct aspect of fitness data (e.g., connection, daily summary, heart rate, sleep, workouts), with no overlapping purposes. Agents can easily differentiate them.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (get_/query_/sync_) with clear prefixes for type of operation, making them predictable and easy to navigate.

    Tool Count5/5

    10 tools is well-scoped for a fitness data server, covering essential data types without being overwhelming or too sparse.

    Completeness4/5

    The tool set covers core fitness data areas (profile, activity, sleep, heart rate, workouts) and includes sync functionality. Minor gaps like nutrition or stress data exist, but the surface is largely complete for typical use.

    Resources