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Mi Fitness MCP

Mi Fitness MCP

by aestilog
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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    • Mi Fitness MCP CN

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      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.
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    • Mi Fitness Data Bridge

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      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.
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    • mifit

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      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.
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    • Mi Fitness MCP CN

      This-weiyuqing
      A
      license
      C
      quality
      B
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      Enables AI clients to query and analyze your own Xiaomi/Mi Fitness health data — daily activity, minute-level heart rate, sleep stages, workouts, body composition, SpO₂, stress, and abnormal heart beat events — after syncing it from the Xiaomi cloud into a local SQLite database. Exposes 14 tools covering connection status, incremental sync control, and metric/date-range queries, with everything stored and served locally.
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    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

    C2.6/5.0

    Scored across 14 tools

    Disambiguation4/5

    Most tools target clearly distinct resources or actions. The generic query_metric_series could overlap with specific metric tools like query_heart_rate or query_spo2, and sync_data/get_sync_status/get_connection_status have related sync semantics, but they remain distinguishable.

    Naming Consistency5/5

    All names use snake_case and follow a predictable verb_noun or get_noun pattern. The mix of get_*, query_*, and sync_* is semantically coherent and consistent across the set.

    Tool Count5/5

    14 tools is well-scoped for a fitness data integration. Each major metric has a dedicated query tool without excessive or redundant tooling.

    Completeness4/5

    The surface covers connection, sync, profile, daily summaries, data coverage, and major health metrics including heart rate, sleep, workouts, SpO2, stress, and body measurements. Minor gaps may exist for device details or granular activity segments, but core coverage is strong.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing

    Resources