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"Open-Source MCP Server in Python with PostgreSQL and OAuth 2.0" matching MCP servers:

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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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    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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    Exposes WHOOP recovery, sleep, strain, and workout metrics to MCP-compatible AI assistants using OAuth 2.0 authentication, enabling daily wellbeing snapshots, trend analysis, and workload recommendations.
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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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    MIT
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    An MCP server that provides AI agents with secure, read-only access to personal Strava fitness data, including activities, performance stats, and more through 21 MCP tools.
    MIT
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    Provides read-only access to Oura ring biometrics via the Oura API, enabling Claude to query daily summaries, sleep, readiness, stress, workouts, baselines, and heart rate data. Designed to complement a Strava connector for joint analysis of training and recovery.
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    MIT
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    MCP server to read daily activity, sleep, heart rate, and body metrics from Google Health API, allowing AI assistants like Claude to access your health data. Optionally syncs health metrics to an Obsidian vault.
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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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    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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    MIT