Workout Tracker MCP Server
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- AlicenseNot gradedqualityDmaintenanceA comprehensive AI-powered fitness tracking application that enables AI tools to interact intelligently with user fitness data, providing personalized workout plans, nutrition tracking, and progress analysis through natural language.15MIT
- AlicenseNot gradedqualityBmaintenanceEnables creating workout plans, tracking progress, suggesting exercises, and calculating training volume through natural language, compliant with MCP protocol.MIT
- FlicenseNot gradedqualityBmaintenanceEnables workout tracking and coaching within Claude conversations, managing exercise configs, logs, streaks, and health metrics via an MCP server with PostgreSQL.-
- AlicenseNot gradedqualityBmaintenanceSelf-hosted fitness tracking MCP server that gives AI assistants access to your nutrition, training, weight, sleep, and accomplishment data via 77 tools and 5 resources. Enables natural-language logging and querying of personal health metrics through Claude or ChatGPT.1BSD 2-Clause "Simplified"
- AlicenseNot gradedqualityCmaintenanceConnects AI assistants directly to Hevy workout data, enabling analysis of training volume, strength progression tracking, and web searches for fitness research.MIT
- AlicenseAqualityAmaintenanceThis MCP server enables logging workouts in plain language through AI assistants and provides tracking of sessions, programs, records, estimated 1RM, and per-muscle load, with coaching informed by the user's logged data.20MIT
TDQS
Scored across 14 tools
log_workout and log_workout_session_to_dynamodb both log workouts but at different levels of detail and persistence, creating real ambiguity for an agent. The multiple get_exercises_by_* filters are largely distinct but could be confused with search_exercises, especially since they all return exercise lists.
Tool names consistently use snake_case with a verb_noun pattern, such as list_*, get_*, log_*, and save_*. Minor deviations like calculate_volume (no resource object) and the long 'to_dynamodb'/'from_dynamodb' suffixes are still readable and predictable.
With 14 tools, the count is on the higher end but still within a reasonable scope for an exercise database combined with workout plan and session logging. A few tools, especially the redundant log_workout, could be consolidated, but the overall count is not excessive.
The server provides save/get for workout plans and a session logger, but lacks list, update, and delete operations for plans or logs. There is also no way to retrieve a user's workout history, leaving significant lifecycle gaps that would cause agent failures in common tracking workflows.