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MCP Connectors

"A search for a string containing SQL injection and XSS payload characters" matching MCP servers:

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    Exposes Garmin Connect health and activity data (steps, sleep, heart rate, etc.) via MCP tools, with built-in login and MFA support.
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    Provides a unified MCP interface to a personal health database, enabling AI agents to sync lifting workouts from Hevy, log food intake against a catalog, and run read-only SQL queries to cross-reference training and nutrition data.
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    A TypeScript server that acts as a bridge to the Strava API, enabling LLMs to access users' activities, routes, segments, and athlete data through natural language interaction.
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    MIT
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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.
    27
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    MIT
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    MacroFactor MCP is a local Fueling & Recovery Decision Engine. Give your MCP client a MacroFactor export, optionally add Garmin, and ask for a decision instead of manually comparing nutrition, sleep, body, and training screens.
    29
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    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.
    4
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    MIT
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    MCP server integrating Foodvisor nutrition API for food search, meal logging, daily summaries, and progress tracking via LLM agents like Claude.
    MIT
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    Enables Claude to access your MyFitnessPal diary—foods, portions, meal and daily nutrition totals, water, exercise, steps, and weight—through a custom connector on web, desktop, and mobile.
    34
    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 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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    Exposes Whoop fitness data (recovery, sleep, strain, workouts) to Claude for use as a daily training coach, enabling natural language queries about your health metrics and training readiness.
    MIT
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    Enables querying personal data synced from services like Lunch Money and Strava using SQL via Claude.
    13
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    MIT