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    Aggregates and analyzes fitness data from multiple sources like Whoop and Strava through a modular adapter architecture. It enables users to monitor health metrics, track activities, and gain insights into sleep, recovery, and training performance.
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    MIT
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    Connects AI agents to Iridium fitness data to query workout history, nutrition logs, and body measurements. It enables users to track exercise progress, training volume, and personalized trainer analysis through natural language.
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    Enables 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.
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    Enables users to interact with their Strava data through natural language to analyze workouts, track fitness progress, and explore routes. It supports retrieving detailed activity stats, heart rate data, and segment insights directly within AI assistants.
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    Connect TrainingPeaks to Claude and other AI assistants via the Model Context Protocol to query workouts, build structured intervals, manage calendar, track fitness trends, and control training through natural conversation.
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    MIT
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    Provides comprehensive access to Strava API for marathon training and race analysis, enabling language models to query athlete data, analyze training patterns, track performance metrics, monitor heart rate zones, and detect injury risks.
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    MIT
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    A Model Context Protocol (MCP) server that provides AI assistants with access to the Hevy fitness tracking API. This allows you to log workouts, manage routines, browse exercises, and track your fitness progress directly through AI chat interfaces.
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    MIT
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    MCP server over the YAZIO food diary that lets a model read what you've eaten today, log meals, build recipes from tracked products, track water, weight and exercise, and query nutrition summaries, goals and profiles.
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    MIT
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    Connect MyFitnessPal to Claude or any MCP client. Log meals, search food database with macros, track trends, and export nutrition history against your real MyFitnessPal diary.
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    MIT
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    Enables users to access Whoop biometrics including recovery, sleep, and strain scores while facilitating activity logging and device management. It allows for seamless interaction with health data to set alarms, update weight, and track activities through natural language commands.
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    MIT
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    Python MCP server for the Hevy fitness app. Gives Claude full access to your Hevy data. Log workouts, manage routines, track body measurements, browse exercises, and more. Covers all 25 endpoints of the official Hevy API.
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    MIT
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    Connect Pelaris to any MCP-compatible AI assistant for personalised fitness coaching. Plan training programs, log workouts, track benchmarks, manage goals, and get data-driven coaching insights. Supports science-based methodologies including 5/3/1, Pfitzinger, polarised training, and more. OAuth 2.0 authentication with Streamable HTTP transport. Documentation: https://pelaris.io/integrations Web
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    Connects to a Gym Tracker Supabase database to provide LLMs with access to personal workout history, routines, and training progress. It enables users to analyze fitness performance, track personal records, and receive personalized coaching advice through natural language.
    Last updated