
Garmin MCP Server
Enables ChatGPT to access and analyze personal Garmin health data including daily steps, heart rate, calories, sleep duration, and body battery levels. Collects data via webhook from Garmin devices and provides health insights through natural language queries.
README
Garmin MCP Server
A webhook receiver and MCP (Model Context Protocol) server that collects Garmin health data and makes it available to ChatGPT through OpenAI's Remote MCP integration.
Features
- Garmin Webhook Receiver: Accepts health data from Garmin devices
- SQLite Persistence: Stores data in SQLite database on EFS for reliability
- MCP Integration: Provides health data to ChatGPT via OpenAI's Remote MCP
- AWS Deployment: Runs on ECS Fargate with Terraform infrastructure
- Rate Limiting: Protects webhook endpoints from abuse
Architecture
Garmin Device → Webhook → SQLite DB → MCP Server → ChatGPT
Data Collected
- Daily step count
- Resting heart rate
- Active calories burned
- Sleep duration
- Body battery levels
Quick Start
Local Development
- Clone the repository:
git clone https://github.com/yourusername/garmin-mcp.git
cd garmin-mcp
- Install dependencies:
npm install
- Set up environment variables:
cp env.example .env
# Edit .env with your configuration
- Build and run:
npm run build
npm start
Docker
docker-compose up --build
Local HTTPS (Cloudflare Quick Tunnel)
You can expose your local server with a temporary public HTTPS URL (no DNS or account needed):
brew install cloudflared
./scripts/cloudflared-quick.sh
This prints a https://<random>.trycloudflare.com
URL. Use it for testing endpoints:
- Webhook:
https://<random>.trycloudflare.com/garmin/webhook
- Health:
https://<random>.trycloudflare.com/healthz
Environment Variables
Variable | Description | Required |
---|---|---|
PORT |
Server port (default: 8080) | No |
MCP_API_TOKEN |
Bearer token for MCP authentication | Yes |
GARMIN_WEBHOOK_SECRET |
Secret for webhook signature verification | No |
GARMIN_API_KEY |
Garmin Health API key | No |
GARMIN_API_SECRET |
Garmin Health API secret | No |
API Endpoints
Webhook
POST /garmin/webhook
- Receives Garmin health data
MCP (Model Context Protocol)
GET /mcp/tools
- Lists available toolsPOST /mcp/tools/call
- Executes a toolGET /mcp/sse
- Server-sent events endpoint
Health
GET /healthz
- Health check endpoint
MCP Tools
garmin.getDailySummary
Get daily health summary for a user and date.
Parameters:
user_id
(string, required): User identifierdate
(string, optional): Date in YYYY-MM-DD format (defaults to today)
garmin.getRecentDays
Get health data for the last N days.
Parameters:
user_id
(string, required): User identifierdays
(number, optional): Number of days to retrieve (default: 7)
Versioning & Deployment
Versioning Strategy
We use git-based versioning for Docker images:
- Git Tags: Create semantic version tags (e.g.,
v1.0.0
,v1.1.0
) - Docker Tags: Images are tagged with both the git version and
latest
- Rollback: Can easily rollback by updating ECS task definition to use a previous version
Creating a Release
# 1. Create a git tag for the release
git tag v1.0.0
# 2. Build and push with versioning
cd terraform
./build-and-push.sh
# 3. Deploy to ECS
aws ecs update-service --cluster garmin-mcp-cluster --service garmin-mcp-service --force-new-deployment
Version History
- v0.1.0: Initial release with SQLite persistence and integration tests
- Next:
v1.0.0
- Production ready with Cloudflare Tunnel deployment
Rollback Process
# List available versions in ECR
aws ecr describe-images --repository-name garmin-mcp --query 'imageDetails[*].imageTags' --output table
# Update task definition to use specific version
aws ecs update-service --cluster garmin-mcp-cluster --service garmin-mcp-service --task-definition garmin-mcp-task:REVISION_NUMBER
Deployment
AWS with Terraform (Cost Optimized - ~$9/month)
Our infrastructure uses a cost-optimized architecture with Cloudflare Tunnel for secure access:
- ECS Fargate: Single task with 0.25 vCPU, 0.5GB RAM
- No NAT Gateway: Saves ~$33/month by using public IP for outbound only
- Cloudflare Tunnel: Provides secure HTTPS access without public inbound traffic
- EFS Storage: SQLite database persistence (~$0.30/month)
Prerequisites
- Cloudflare Account: Create a tunnel and get the tunnel token
- AWS Credentials: Configure AWS CLI access
- Domain: Optional - for custom hostname instead of trycloudflare.com
Deploy Steps
-
Create Cloudflare Tunnel:
# Install cloudflared brew install cloudflared # Login to Cloudflare cloudflared tunnel login # Create tunnel cloudflared tunnel create garmin-mcp # Get tunnel token (save this) cloudflared tunnel token garmin-mcp
-
Configure Terraform:
cd terraform # Create terraform.tfvars with your tunnel token echo 'cloudflare_tunnel_token = "your-tunnel-token-here"' > terraform.tfvars
-
Deploy:
./deploy.sh
-
Configure Tunnel Route (optional):
# For custom domain cloudflared tunnel route dns garmin-mcp webhook.yourdomain.com
Security Benefits
- No Public Inbound: Security group blocks all incoming traffic
- Outbound Only: Task can make outbound connections (Docker images, Cloudflare)
- Encrypted Tunnel: All traffic encrypted through Cloudflare's edge
- Cost Effective: No NAT Gateway or ALB required
Manual Deployment
- Build the Docker image
- Push to ECR
- Deploy to ECS Fargate
Privacy
This is a personal, non-commercial project. See PRIVACY.md for details on data collection and usage.
License
Personal use only. This project is not intended for commercial use.
Contributing
This is a personal project, but suggestions and improvements are welcome through issues and discussions.
推荐服务器

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。
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。
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。
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。

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。
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。
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。

e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。