Mi Fitness Data Bridge
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- AlicenseBqualityAmaintenanceDownloads 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.48149AGPL 3.0
- AlicenseNot gradedqualityCmaintenanceSelf-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
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- FlicenseAqualityBmaintenanceProvides 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.61-
TDQS
Scored across 15 tools
Most tools have distinct purposes: sync vs. status, profile vs. daily summary, and each query_* targets a specific data type (heart rate, sleep, workouts, etc.). However, query_metric_series overlaps conceptually with the specific query tools, and its relationship to them is not fully clear from descriptions alone.
The majority follow a clear get_/query_ + noun pattern (e.g., get_profile, query_sleep), and sync_data continues the verb_noun style. The one outlier is workout_series, which is a noun phrase and breaks the consistent convention.
Fifteen tools is at the upper bound of the well-scoped range but still reasonable for covering connection, sync, profile, and diverse fitness metrics. The inclusion of both generic and specific query tools creates mild redundancy, but overall the count is not excessive.
The tool set covers the full spectrum of fitness data access: connection, synchronization, profile, daily summaries, raw metrics (heart rate, SpO2, stress), workouts, sleep, and body measurements. It includes both data retrieval and sync management, with no obvious dead ends for typical use cases.