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Hevy MCP Server

@amilz

Repository

Basic information

Category

Other

Transports

stdio

Author

amilz

About Hevy MCP Server

A TypeScript MCP Server for interacting with Hevy Workout App in LLMs

#hevy-mcp#workout-tracking#fitness-assistant

Hevy MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "hevy": {
      "command": "node",
      "args": [
        "/path/to/hevy-mcp/build/src/index.js"
      ],
      "env": {
        "HEVY_API_KEY": ""
      }
    }
  }
}

Hevy MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Hevy MCP?

Hevy MCP is a TypeScript-based Model Context Protocol (MCP) server that connects AI assistants to the Hevy workout tracking API. It enables AI tools to retrieve and analyze your workout history, helping you gain insights into your fitness journey.

How to use Hevy MCP?

Clone the repository, install dependencies (npm install), and build the TypeScript code (npm run build). Configure your Hevy API key in your LLM’s MCP settings (e.g., Claude Desktop’s claude_desktop_config.json), restart the LLM, and then ask queries like “Summarize my last 5 workouts.”

Key features of Hevy MCP

  • Retrieves user workout history from the Hevy API
  • Implements the Model Context Protocol for AI assistant integration
  • Simple setup with configurable options
  • Provides the getWorkouts tool with pagination support

Use cases of Hevy MCP

  • Ask an AI assistant to summarize your last several workouts
  • Get recommendations for today’s workout based on recent training data
  • Analyze workout patterns or progress over time

Frequently asked questions

1

What does Hevy MCP do that alternatives don’t?

The README does not compare this server to alternatives. It focuses on integrating Hevy workout data with any MCP‑compatible AI assistant.

2

What are the runtime requirements?

Node.js v18 or higher, a Hevy API key (obtainable from Hevy Settings), and an LLM that supports the Model Context Protocol (e.g., Claude Desktop).

3

Where does my workout data live?

Workout data is stored on Hevy’s servers. This server retrieves it from the Hevy API; it does not store data locally or transmit it elsewhere beyond your AI assistant.

4

Are there any known limitations?

Currently, the server only provides a single tool (`getWorkouts` with pagination). The author notes that additional methods may not be useful with an LLM and invites ideas via issues or PRs.

5

How is authentication handled?

Authentication uses a Hevy API key supplied as an environment variable (`HEVY_API_KEY`) in the LLM’s MCP configuration. No other transport or auth mechanisms are documented.

Comments

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