Hevy MCP
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Related Servers
- AlicenseNot gradedqualityCmaintenanceA 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.14 npmMIT
- AlicenseAqualityDmaintenanceAn MCP server that interfaces with the Hevy fitness tracking API, enabling AI assistants to manage workouts, routines, exercise templates, and more via natural language.2679,651 npmMIT
- AlicenseAqualityCmaintenanceMCP server for the Hevy fitness tracking app, enabling management of workouts, routines, exercise templates, folders, and webhooks through AI assistants.2579,651 npmMIT
- FlicenseNot gradedqualityDmaintenanceAn MCP server that connects AI clients to the Hevy workout tracking app, allowing users to manage routines and exercises. It enables reading workout history and logging new fitness sessions through simple natural language commands.-
- AlicenseNot gradedqualityCmaintenancePython 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.MIT
- AlicenseBqualityDmaintenanceMCP server for the Hevy workout tracker API that allows reading and writing workouts, routines, exercise templates, and body measurements.2211 npmMIT
TDQS
Scored across 23 tools
Every tool has a distinct purpose and clear naming: singular vs plural separates detail views from list views, and each resource type has its own verb-noun pair. Even similar tools like get-exercise-templates and search-exercise-templates are distinguishable by their retrieval mode.
Tool names consistently follow a verb-noun pattern (get, create, update, search) with snake_case and predictable singular/plural distinctions. The naming is uniform across workouts, routines, folders, exercise templates, and body measurements.
23 tools is on the heavy side, but the count is structured across several coherent subdomains (workouts, routines, templates, measurements). It feels somewhat large but not bloated or redundant.
The set covers read and create/update operations broadly, but there are no delete operations for any resource type, and exercise templates lack update/delete. Those are significant lifecycle gaps for a CRUD-oriented API, likely causing agent failures when cleanup or correction is needed.