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    Enables recording meals and workouts in Traditional Chinese and writing them to a user's Fitbit/Google Health account via Google Health API v4, with local OAuth, dry-run previews, and idempotency safeguards.
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    MIT
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    Enables users to describe workouts in plain English and have them created as structured Garmin Connect workouts with pace, HR, power, cadence targets, and repeat blocks. Also provides full workout lifecycle management including listing, updating, deleting, and scheduling to the calendar.
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    72 npm
    MIT
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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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    AGPL 3.0
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    MCP server that lets you ask questions about your Garmin training history in plain language. It ingests FIT files into DuckDB locally and exposes typed tools for analyzing activities, streams, and summaries.
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    MIT
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    Ask your Oura Ring about sleep, readiness, activity, stress and heart rate in ChatGPT or Claude, in any language. Read-only, 10 task-oriented tools; runs in a sandbox demo mode with no credentials.
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    20 npm
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    MIT
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    Enables an MCP client to read and write a personal Garmin Connect account: activities with per-set strength detail, daily health metrics (sleep, HRV, stress, steps, readiness), planned and completed workouts, and weigh-ins with body composition. It also logs finished strength sessions and body-composition readings as FIT files so they appear in Garmin Connect like natively recorded ones.
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    250 PyPI
    2
    MIT
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    Query Apple Health export data with an LLM via a semantic layer that avoids common data traps, enabling accurate natural-language queries about health metrics. Optionally imports CPAP therapy data for correlation.
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    MIT
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    Provides six deterministic, source-attributed hydration calculators (water intake, dehydration check, pregnancy intake, kidney-safe intake, athlete plan, energy optimization) as MCP tools, enabling AI clients to call them via stdio without custom HTTP coding.
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    39 npm
    MIT
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    This MCP server enables logging workouts in plain language through AI assistants and provides tracking of sessions, programs, records, estimated 1RM, and per-muscle load, with coaching informed by the user's logged data.
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    MIT
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    MCP server for the Google Health API: heart rate, activity, sleep, SpO2, HRV, ECG and irregular-rhythm notifications, read into a local SQLite cache for fast offline queries and trend analysis. OAuth 2.0 with automatic token refresh, incremental sync, a cache-only offline mode, and a doctor command that diagnoses a setup without spending quota.
    24
    184 PyPI
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    GPL 3.0
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    MCP server for Bosch eBike Flow (Smart System / BES3). Read-only access to battery health and charge-cycle trends, components and firmware, live state-of-charge, and per-ride activity data (distance, elevation, power, assist-mode, CO2), signing in with your own Bosch account via PKCE.
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    239 PyPI
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    GPL 3.0
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    Enables an LLM to search a movement library, log single-movement and multi-movement workout results, and pull complete logged training history plus PR trends for Beyond the Whiteboard straight from a chat. All logged entries are posted privately, and session authentication can refresh automatically.
    7
    MIT
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    Enables an agent to fetch one night of Eight Sleep data as ready-made text — scores, sleep stages, HRV, heart and respiratory rate versus your averages, and bed/room temperatures — plus short summaries of earlier nights for a morning check-in. Read-only, so nothing on the mattress can be changed.
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    MIT
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    Provides Claude Code and Claude Desktop with safe, read-only access to your local Garmin training data, readiness decisions, run details, and trends so you can get coaching advice in natural language.
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    MIT
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    A local-first, read-only MCP server that provides compact recovery, sleep, strain, HRV, heart-rate, workout, and body-measurement data from WHOOP without sending credentials to a third party.
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    92 npm
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    Enables AI assistants to query a user's own Garmin Connect training, health, and recovery history through Garmin's official Personal API, backed by a local cache and built-in analysis tools.
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    379 npm
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    Apache 2.0
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    Connects AI assistants to SmartGym on Mac, enabling users to review workouts, tweak routines, and build training programs in plain English, with changes syncing to iPhone and Apple Watch.
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    MIT
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    Archives Garmin Connect health data locally (steps, HR, sleep, body battery) as JSON, no cloud, and also pulls context data (weather, pollen, air quality). Built-in MCP server (with SQLite cache) exposes the archive for querying via LLM tool calls.
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    A read-only MCP server for the WHOOP API v2 that lets you query and analyze your own recovery, sleep, strain, cycles, and workout data. Note: currently a pre-alpha scaffold with stubbed internals.
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    Enables read-only access to your own Withings health data through a personal developer app, exposing body measurements, blood pressure, daily activity, sleep summaries, and linked devices for natural-language queries.
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    MIT
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    Enables AI assistants to read your SisRUN training plan with your own credentials and answer questions about prescribed workouts, like weekly coach plans, in a structured format.
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    MIT
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    Enables querying Anytime Fitness club occupancy, hourly forecasts, nearby clubs, and personal visit history through natural language.
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    Unofficial MCP server for Fitatu, the Polish calorie counter: read and log food, weight, water, body measurements and workouts, check calories and macros left, set goals and get weekly reports. 47 tools (26 read-only), read-only and demo modes, stdio or HTTP with a token, free and Premium accounts.
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    138 PyPI
    21
    MIT
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    Minimalist Strava integration for Claude with 7 focused tools for querying athlete stats, activities, zones, and segment analysis.
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    18 npm
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    MIT
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    An MCP server that provides access to detailed exercise-to-muscle mapping data for over 320 exercises and 65 muscles. It enables users to analyze workout coverage, find exercise alternatives based on muscle overlap, and search for specific exercise activation data.
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    42 npm
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    MIT