Samsung Health Analytics MCP
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- AlicenseBqualityDmaintenanceEnables Claude to answer natural language questions about your Apple Health data by analyzing local CSV exports, supporting metrics like steps, heart rate, sleep, and more.62MIT
- AlicenseAqualityDmaintenanceProvides Claude with real-time access to local health data including sleep, recovery, strain, and workouts from WHOOP and Apple Health, enabling informed context-aware interactions.61MIT
- AlicenseAqualityDmaintenanceEnables Claude Desktop to access and analyze Garmin wearable health data including sleep, HRV, Body Battery, and activity metrics. Users can query their health trends, track recovery, and generate interactive HTML dashboards using natural language.96MIT
- AlicenseAqualityCmaintenanceEnables Claude Desktop and Claude Code to read locally synced Fitbit Air health data — scores, trends, sleep, activity, running and body age — run read-only SQL against it, and keep a journal and coach notes on the user's Mac. It also offers profile edits, a refresh tool, and guided prompts such as morning check-ins, weekly reviews, race planning and score explanations.171Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables Claude clients to read Oura Ring sleep, heart rate, readiness, and activity data; log, delete, and query home glucose, ketone, weight, blood pressure, and nutrition readings; and ingest Apple Health data via Health Auto Export.33 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables read-only access to WHOOP recovery, sleep, HRV, strain, and workout data so users can ask natural-language questions about their personal health history, trends, and baselines inside Claude, Codex, or GitHub Copilot. It also provides HYROX and triathlon session analysis, race readiness checks, and a local performance dashboard.7 npmMIT
TDQS
Scored across 16 tools
Each tool targets a distinct analysis surface (trends, periods, trips, sleep, workouts, SQL). There is mild overlap between overview and dashboard_payload (both summarize the dataset), and between trips and detect_locations (both derive travel), but descriptions make the split between summary/payload and analysis/raw-timeline reasonably clear.
All names are snake_case and readable, but the convention is mixed: several are verb_noun (ingest_export, set_periods, render_dashboard, compare_periods, run_sql) while others are bare nouns (overview, trips, workouts, schema, sleep_timing). The inconsistency is minor but visible across 16 tools.
16 tools is just over the comfortable band but justified by a genuinely broad analytics domain (ingestion, overview, trends, profiles, comparisons, travel, SQL escape hatch, dashboard). No single tool looks trivially redundant, though the overview/dashboard_payload pair could likely merge.
Coverage is strong: ingestion, schema inspection, raw SQL, period definition, trends, comparisons, profiles, sleep, baselines, trips, workouts, and dashboard output. Minor gaps exist around exporting analysis results or persistent saved analyses, but agents can work around them via run_sql.