davidmosiah/google-health-mcp

Description and use

**Brief Summary (2‑3 sentences)**
davidmosiah/google-health-mcp is a TypeScript‑based, local‑first Model Context Protocol (MCP) server that wraps the Google Health API v4 (including Fitbit and Pixel Watch data) behind a standard, AI‑friendly interface. By exposing health‑track data through a uniform MCP endpoint, it lets LLM‑powered assistants and other AI agents query real‑world fitness metrics without bespoke integrations. The project is actively maintained, has growing community interest, and is positioned as a ready‑to‑pilot OSS component for AI‑tool orchestration.

**Value**

• **Standardized AI‑to‑tool bridge** – The MCP layer abstracts the quirks of the Google Health SDK, letting any MCP‑compatible agent (Claude, Cursor, Hermes, etc.) interact with health data via simple request/response semantics.
• **Local‑first privacy** – The server runs on the user’s device, keeping sensitive health metrics off the cloud while still enabling powerful AI reasoning.
• **Reusability across agents** – Once deployed, the same endpoint can serve multiple assistants, reducing duplicated integration effort and fostering a plug‑and‑play ecosystem.

**Practical Adoption Path**

1. **Spin‑up** – Clone the repo, run npm install && npm run start (or use the provided Dockerfile) on a trusted machine that already has Google Health OAuth credentials.
2. **Configure** – Add the required Google OAuth client ID/secret and grant the necessary scopes (Fitbit, Wear OS).
3. **Connect** – Point any MCP‑compatible AI agent to the server’s URL (http://localhost:PORT/mcp) and use the documented MCP methods (getUserMetrics, listDevices, etc.).
4. **Extend** – Leverage the TypeScript SDK to add custom health queries or combine with other MCP services (e.g., calendar, notes) for richer multimodal agents.
5. **Deploy** – For production, containerize the server, place it behind a zero‑trust gateway, and enable persistent storage for refresh tokens.

**Production Readiness**

• **Activity & Community** – 23 ★, 5 forks, recent commits (last updated 2026‑07‑12), and 18 relevant topics indicate an engaged, albeit small, community.
• **Technical Maturity** – Written in TypeScript with clear API contracts, a CLI for local testing, and Docker support, the codebase is easy to audit and integrate.
• **Security Posture** – No known vulnerabilities; the local‑first design limits exposure, but a final review of the MIT‑style license and token handling is advisable.
• **Scalability** – Suitable for individual‑ or small‑team pilots; for larger deployments you’d need to add rate‑limiting, token refresh automation, and monitoring.

Overall, the project is a solid OSS candidate for pilots that need trustworthy, real‑time health data within AI assistants, and it can be moved to production after a brief security and licensing audit.

Use in a workflow

davidmosiah/google-health-mcp helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.