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Hevy MCP

Hevy MCP

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github.com/amilzHealth2Updated 77w ago
Repository

About Hevy MCP

Hevy MCP is a TypeScript MCP server for AI assistants to retrieve and analyze workout data from the Hevy tracking API. Its current tool fetches paginated workout history.

Key features

  1. 01Retrieve Hevy workout history
  2. 02Fetch workouts with pagination
  3. 03Connect AI assistants through MCP

Use cases

  1. 01Summarize recent workout sessions
  2. 02Review fitness progress from workout history

MCP support

It exposes Hevy workout data to MCP clients through the `getWorkouts` tool, which retrieves user workouts with pagination.

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

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Hevy MCP

A TypeScript Model Context Protocol (MCP) server implementation for interacting with the Hevy workout tracking API.

Overview

Hevy MCP provides AI assistants with access to your Hevy workout data through the Model Context Protocol. This server enables AI tools to retrieve and analyze your workout history, helping you gain insights into your fitness journey.

Hevy MCP

Features

  • Retrieves workout history from Hevy API
  • Implements the Model Context Protocol for AI assistant integration
  • Simple setup with configurable options
API Tools

At present, the MCP server provides the following tools:

  • getWorkouts: Retrieve user workouts with pagination

(I haven't convinced myself that additional methods will be useful with the LLM--if you have ideas/thoughts, drop an issue or PR!)

Requirements

Installation

Clone the repository

git clone https://github.com/amilz/hevy-mcp.git && cd hevy-mcp

Install dependencies

npm install

Build the TypeScript code:

npm run build

Configuration

Before using the application, you need to set up your Hevy API key in your LLM (example for Claude Desktop). Your Claude Desktop config file should look like this:

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

Usage

Restart your LLM Environment to apply the changes. Try a simple query like "Summarize my last 5 workouts" or "based on my last 10 workouts, could you recommend a workout for today?"

Dependencies

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.