Skip to main content
Glama

Related Servers

Alternatives to Mi Fitness Data Bridge

No user-submitted related servers found.

    Related Servers

    • F
      license
      Not graded
      quality
      C
      maintenance
      Enables AI to query Xiaomi Mi Band health data (steps, sleep, heart rate) from Gadgetbridge SQLite exports via the MCP protocol, allowing natural language questions about daily activity.
      1
      -
    • A
      license
      C
      quality
      C
      maintenance
      Enables reading and syncing Xiaomi Mi Fitness health data (steps, heart rate, sleep, workouts) from the Chinese cloud region to a local SQLite database via MCP tools.
      10
      14
      MIT
    • A
      license
      B
      quality
      A
      maintenance
      Downloads all your Garmin health and fitness data into a local SQLite database and exposes 45 MCP tools for AI analysis, enabling assistants to query sleep, training load, HRV, and more.
      48
      165 PyPI
      153
      AGPL 3.0
    • 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
    • A
      license
      A
      quality
      A
      maintenance
      MCP server that reads Zepp/Amazfit health and workout data, exposing tools for daily summaries, sleep, heart rate, and workout details to any MCP client.
      8
      3
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables MCP clients to query locally computed recovery, strain, sleep, workout, and heart-rate data from a personal SQLite database, with tools for daily and sleep summaries, workout details, heart-rate buckets, schema inspection, reports, sync, and read-only SQL.
      8
      MIT

    TDQS

    A4.6/5.0

    Scored across 17 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: query_* tools target specific health data types or aggregation levels, while sync lifecycle tools (connection, sync, status, cancel, history, coverage) are well separated. Descriptions explicitly cross-reference related tools to resolve potential overlaps (e.g., metric series vs daily activity vs raw body measurements).

    Naming Consistency5/5

    All names use consistent snake_case with predictable verb prefixes: get_ for system/status reads, query_ for cached data reads, and sync/cancel for actions. Minor singular/plural variation (query_workouts vs query_workout_series) is semantically meaningful and not confusing.

    Tool Count4/5

    17 tools is slightly above the typical 3–15 range but justified by the breadth of distinct health metrics and sync management operations. No tool appears redundant; each query tool maps to a unique data type or purpose.

    Completeness4/5

    The surface covers connection, sync lifecycle, cache coverage, profile, and queries for all major Mi Fitness data types (activity, body, heart rate, sleep, workouts, SpO2, stress, abnormal beats). Minor gaps like sleep stages or workout GPS details may exist, but agents can work around them.

    Maintenance

    ActivityActive
    ResponsivenessWithin a week