Hevy MCP

Name: Hevy MCP
Author: amilz

@amilz

代码仓库

关于 Hevy MCP

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

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

基本信息

分类

其他

运行时

node

传输方式

stdio

发布者

amilz

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

FAQ from Hevy MCP

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.

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).

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.

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.

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.