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    Enables AI agents like Claude Desktop, Cursor, and VS Code to connect directly to COLMI R02/R03 and compatible smart rings over local Bluetooth LE, exposing tools for ring scanning, battery and firmware status, activity and sleep/recovery insights, real-time heart rate and SpO2 vitals, 3-axis motion sampling, clock sync, and physical LED blinking. Runs fully locally with no vendor cloud, accounts, or subscriptions.
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    MIT
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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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    Enables AI assistants to read and analyze Google Fit exported data (activities, daily metrics, workouts, sleep) from Google Takeout, providing tools for queries, resources, and coaching prompts.
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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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    25 npm
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    MIT
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    Enables AI agents to interact with a personal YAZIO nutrition diary, including searching products, reading and writing diary entries, and analyzing nutrition trends.
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    162 npm
    MIT
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    MCP server for the Hevy fitness tracking app, enabling management of workouts, routines, exercise templates, folders, and webhooks through AI assistants.
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    53,090 npm
    MIT
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    An MCP server for the Polar AccessLink API. Connect your Polar fitness data to Claude AI - access workouts, sleep analysis, recovery metrics, heart rate data, and more.
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    A production-ready MCP server for the Hevy fitness API that exposes 27 tools for reading, writing, and analyzing workout data, enabling LLM-driven personal trainer workflows.
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    MIT
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    A local, read-only Model Context Protocol server for an athlete's Strava profile and activities, enabling retrieval of profile data, activity lists (with paging and date filters), and detailed activity information via standard input/output for MCP hosts.
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    11 npm
    MIT
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    Enables 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.
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