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"Accessing Hevy workout tracking app data" matching MCP servers:

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    A Model Context Protocol (MCP) server implementation that interfaces with the Hevy fitness tracking app and its API. This server enables AI assistants to access and manage workout data, routines, exercise templates, and more through the Hevy API (requires PRO subscription).
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    MCP server for the Hevy workout tracker API that allows reading and writing workouts, routines, exercise templates, and body measurements.
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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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    Exposes the Hevy workout API to Claude, enabling users to manage workouts, routines, exercise templates, body measurements, and user info via natural language.
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    An MCP server that interfaces with the Hevy fitness tracking API, enabling AI assistants to manage workouts, routines, exercise templates, and more via natural language.
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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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    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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    Enables generating presentations, documents, webpages, and social posts via Gamma's API with full parameter surface including templates, folders, headers/footers, and email sharing.
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    MCP server that brings interactive UIs for Yida low-code platform into AI chat conversations, enabling app management, form schema preview, and report chart visualization.
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    Enables natural language search and discovery of open-access scientific datasets through the EOSC Data Commons OpenSearch service. Provides tools to search datasets and retrieve file metadata using LLM-assisted queries.
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    MIT