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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
    1
    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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    A lightweight Garmin Model Context Protocol server with 12 curated endpoints for endurance athletes, enabling natural-language queries of activity, training, health, device, and goal data from Garmin Connect.
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    175 PyPI
    MIT
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    Enables AI assistants like Claude to interact with TrainingPeaks, allowing users to query workouts, build structured intervals, track fitness trends, and add comments through natural language, with automatic secure login.
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    MIT
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    Enables access to WHOOP fitness and health data through all WHOOP v2 API endpoints. Supports OAuth 2.0 authentication and provides comprehensive access to user profiles, physiological cycles, recovery metrics, sleep analysis, and workout data.
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    22 npm
    16
    MIT
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    Enables searching FatSecret's food and recipe database, and managing personal food diary, weight, exercise, saved meals, and favorites, with multi-user support and secure OAuth authentication.
    28
    Apache 2.0
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    Provides AI agents with access to Hevy workout data, allowing them to manage workouts, routines, exercises, and body measurements through a Dockerized SSE endpoint.
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    A Model Context Protocol server that provides language models with access to Strava API data, allowing them to query and analyze athlete activities from Strava.
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    MIT
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    Exposes 24 official Garmin personal API endpoints as MCP tools, letting users query daily health and wellness data (stress, steps, sleep, HRV, body battery, SpO2, activities) and download raw activity files for China or international regions. Data is returned as-is from Garmin's official client, with credential-based single-account setup.
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    MIT
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    Provides local, Keychain-secured access to all 26 Garmin Connect Personal API GET endpoints plus 8 summary tools. Enables users to query sleep, HRV, Body Battery, activities, profile, weight, and other connected health data through their own Personal API key.
    34
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    Apache 2.0
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    Enables AI assistants to read training, sleep, recovery, nutrition, body composition, race and goal data, and with permission write workouts, daily targets, meal plans, logged meals and notes. Also supports checking for updates since the last look.
    MIT
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    Connects Aidlab wellness data to AI tools through MCP, providing read-only access to daily summaries, steps, heart rate, HRV, sleep, activities, and profile with OAuth authentication.
    MIT
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    This MCP server exposes read-only Apple Health data to AI assistants via three tools (health_now, health_detail, health_trends), deployable to Cloudflare with a one-click button and no coding required.
    MIT
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    Gives any MCP client local access to Garmin Connect sleep and nap data, recovery and Body Battery, training readiness and load, running tolerance, VO2 Max, race predictions, gear mileage and running summaries. It runs over stdio or Streamable HTTP and includes a credential-free demo mode with fictional sample data.
    2
    MIT
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    A Python-based Model Context Protocol server that provides AI assistants with access to Oura Ring health and fitness data across 15+ endpoints. It enables querying metrics like sleep, activity, readiness, and cardiovascular health through simple authentication and date-range filtering.
    MIT
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    Enables MCP clients to read and update Garmin Connect data, including activities, health metrics, training status, and more, with per-account multi-tenant authentication.
    3
    MIT
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    Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.
    AGPL 3.0
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    Bridges the official COROS (高驰) MCP endpoint into DeepSeek Harness or any MCP client, exposing 34 tools covering sleep, HRV, resting heart rate, recovery, training load, activities and FIT files, plus workout and training-plan creation and scheduling. It handles OAuth login, rotation-safe token refresh and MCP schema inlining locally, so users can query and manage their COROS training data through natural language with no external CLI or runtime dependencies.
    MIT
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    Enables Claude to create, list, inspect, and delete custom rowing workouts in the EXR indoor rowing app, converting intervals.icu workout documents (power, cadence, ramps, repeats) into EXR's simpler timed or distance-based blocks with dates in the titles. Runs as an AWS Lambda-hosted claude.ai custom connector with per-user OAuth sign-in, so several people can share one deployment while each only sees their own EXR workouts.
    1
    MIT
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    Enables MCP clients to access and manage Intervals.icu training data, including activities, calendar events, wellness metrics, power curves, gear, and custom items, with zero-config interactive authentication.
    38 npm
    MIT
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    MCP server that mirrors your Garmin data into a personal database and exposes tools for health summaries, training load, muscle readiness, and race analysis, with optional chat-driven insights via stdio or HTTP.
    1
    MIT
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    Enables MCP-compatible clients to search foods and manage a personal nutrition diary, saved meals, exercise, weight, favorites, recipes, and monthly summaries via the FatSecret Platform API. It provides 39 tools with confirmation for mutating actions, OAuth 1.0 3-legged authentication, and MCP Streamable HTTP transport.
    MIT
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    MCP server that turns any MCP-compatible LLM client into a wellness coach grounded in verified public data, offering nutrition, exercise, biometric, and preventive care tools with safety guardrails.
    MIT
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    Enables MCP-capable AI assistants to read and safely update a self-hosted pantry, food diary, recipes, training history, Garmin data, shopping needs, and reminders through typed tools with server-enforced guardrails.
    MIT