FitBit MCP Server

Fitbit MCP is an unofficial Model Context Protocol implementation that bridges the gap between Fitbit’s public API and AI assistants such as Claude or other MCP‑compatible agents. By exposing a rich set of tools that mirror Fitbit’s data endpoints, the server lets developers query health and fitness metrics directly from within conversational or analytic workflows. This eliminates the need for custom API wrappers, streamlining integration and reducing boilerplate code.

The server solves a common pain point for developers working with wearable data: the complexity of OAuth flows, token management, and JSON mapping. With a single environment variable or command‑line flag, the MCP authenticates against Fitbit’s OAuth 2.0 endpoint and presents a clean, typed interface to the assistant. This means that an AI can ask for “my steps from yesterday” or “average heart rate over the last week,” and receive structured data without any additional parsing logic. The ability to fetch lifetime statistics, device information, and even badge achievements further expands the scope of what an AI can reason about, enabling personalized coaching or trend analysis.

Key capabilities include:

• Comprehensive data access: Tools for activities, sleep logs, heart rate, steps, body measurements, food and water intake, and more.
• Flexible querying: Optional (YYYY‑MM‑DD) and (1d, 7d, 30d, 1w, 1m) parameters allow precise time‑range selection.
• User profile and settings retrieval: Insight into user preferences, device details, and earned badges.
• Lifetime statistics: Aggregate metrics that help in long‑term health monitoring.

Typical use cases span from personalized fitness coaching—where an AI assistant can generate daily summaries and suggest workouts—to research pipelines that aggregate anonymized data for cohort studies. In a customer support scenario, an agent could quickly pull a user’s recent sleep patterns to diagnose insomnia. Because the MCP follows the standard Model Context Protocol, it can be plugged into any AI workflow that supports tool invocation, making it a versatile component in data‑driven health applications.

What sets Fitbit MCP apart is its tight coupling to Fitbit’s official API, ensuring data accuracy and compliance with rate limits. The server also handles token renewal transparently, so developers can focus on building value‑added logic rather than authentication plumbing. By exposing a clean, declarative interface to Fitbit’s rich dataset, the MCP empowers developers to build smarter, context‑aware AI assistants that help users stay healthier and more engaged with their wearable data.