garmin-mcp

garmin-mcp

Exposes Garmin Connect health and activity data (steps, sleep, heart rate, etc.) via MCP tools, with built-in login and MFA support.

Category
访问服务器

README

garmin-mcp

MCP server that exposes Garmin Connect data (activities, steps, sleep, heart rate, body battery, HRV, stress, etc.) as tools, backed by the garminconnect library.

Setup

  1. Copy .env.example to .env and fill in your Garmin Connect credentials:

    cp .env.example .env
    
  2. Install dependencies (already done in .venv):

    .venv/bin/pip install -e .
    
  3. Call the login tool first, before any other tool. It reuses cached session tokens from ~/.garminconnect (override with GARMIN_TOKEN_STORE) if present, otherwise logs in with GARMIN_EMAIL/GARMIN_PASSWORD. If your account has MFA enabled, login returns status: "needs_mfa" — check your email/authenticator app for the code and call submit_mfa_code with it to finish. After a successful login, tokens are cached so future runs skip login/MFA until they expire.

Running via Docker

A prebuilt image is published to Docker Hub as zwisler/garmin-mcp (built by the .github/workflows/docker-publish.yml GitHub Actions workflow on every push to main and on v* tags).

docker run -i --rm \
  --env-file .env \
  -v garmin-mcp-tokens:/data \
  zwisler/garmin-mcp
  • -i keeps stdin open — required, since MCP talks JSON-RPC over stdio.
  • --env-file .env supplies GARMIN_EMAIL/GARMIN_PASSWORD.
  • The named volume persists cached session tokens (GARMIN_TOKEN_STORE defaults to /data/garminconnect inside the image) across container restarts, so you don't have to log in / do MFA every run.

To register this with Claude Code instead of the local venv, point .mcp.json at:

{
  "mcpServers": {
    "garmin-connect": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "--env-file", ".env", "-v", "garmin-mcp-tokens:/data", "zwisler/garmin-mcp"]
    }
  }
}

Running via docker compose

docker-compose.yml builds from the local Dockerfile (falls back to pulling zwisler/garmin-mcp if you skip build), reads .env, and persists tokens in a named volume. Use run, not up — MCP is a one-shot stdio process per client connection, not a long-running background service:

docker compose run --rm garmin-mcp

To register this with Claude Code, point .mcp.json at:

{
  "mcpServers": {
    "garmin-connect": {
      "command": "docker",
      "args": ["compose", "run", "--rm", "garmin-mcp"]
    }
  }
}

Building and pushing manually

docker build -t zwisler/garmin-mcp:latest .
docker push zwisler/garmin-mcp:latest

CI setup

The GitHub Actions workflow needs two repo secrets to push to Docker Hub:

  • DOCKERHUB_USERNAME — your Docker Hub username
  • DOCKERHUB_TOKEN — a Docker Hub access token (Account Settings → Security → New Access Token), not your password

Registering with Claude Code

This repo includes a .mcp.json pointing at the venv's Python interpreter, so Claude Code picks it up automatically when running from this directory.

To register manually elsewhere:

claude mcp add garmin-connect /Users/zwisler/PycharmProjects/garmin-mcp/.venv/bin/python /Users/zwisler/PycharmProjects/garmin-mcp/server.py

Available tools

  • login — call this first
  • submit_mfa_code(code) — only if login returned status: "needs_mfa"
  • get_user_profile
  • get_daily_summary(day)
  • get_steps(day)
  • get_heart_rate(day)
  • get_sleep(day)
  • get_body_battery(start_day, end_day)
  • get_stress(day)
  • get_hrv(day)
  • get_body_composition(day)
  • get_activities(limit, start)
  • get_activities_by_date(start_day, end_day, activity_type)
  • get_activity_details(activity_id)
  • get_activity_splits(activity_id)
  • get_race_predictions
  • get_training_readiness(day)
  • get_respiration(day)
  • get_spo2(day)
  • get_devices
  • get_personal_records

All day parameters accept YYYY-MM-DD and default to today.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选