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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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    MIT
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    Provides comprehensive running performance calculations including VDOT, training paces, race time predictions, velocity markers, and heart rate zones using Jack Daniels, Greg McMillan, and Riegel methodologies.
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    MIT
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    Local MCP server that reorders YouTube Music playlists for running, using YouTube Data API to read, validate, and reorder tracks with dry-run and safe copy creation.
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    Apple HealthKit → PostgreSQL sync + MCP server for Claude Code, Cursor, and Ollama. Open-source iOS app with reliable background sync (HKObserverQuery). 11 tools covering health summary, sleep, HRV trends, workouts, daily snapshots, metric stats, period comparison, and coaching briefs. Supports Supabase, Neon, and self-hosted Postgres. Privacy-first: your data goes to your own database.
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    MIT
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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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    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 retrieval of Garmin Connect data such as activities, sleep, heart rate, and body battery levels using unofficial API through MCP tools.
    4
    MIT
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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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    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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    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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    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