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Access Fitbit API data with OAuth, caching, and trend analysis via MCP.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.partymola/fitbit-mcp" MCP server from askskill: Run: claude mcp add 'io-github-partymola-fitbit-mcp' -- npx -y fitbit-mcp
Connect to my Fitbit account, read the last 30 days of activity data, and summarize steps, active minutes, and trend changes.
A summary of the last 30 days of Fitbit activity data with key trend analysis.
Read my most recently synced Fitbit data from the local cache, avoid duplicate requests, and list the available date range.
An overview of Fitbit data stored in the local SQLite cache and its available date range.
Explain how this Fitbit MCP server uses OAuth PKCE authorization and what I need before using it.
An explanation of the authorization flow, Fitbit Web API prerequisites, and local caching details.
Developers can connect this MCP server to an MCP-compatible AI client so the model can access Fitbit Web API data directly. It fits scenarios where health or activity records need to be queried in conversation.
Data analysts or researchers can use Fitbit data to inspect trends over time. It is suitable for analyzing time-series patterns such as steps and activity levels.
The local SQLite cache is useful when the same Fitbit data needs to be queried repeatedly. It can reduce repeated API access and improve follow-up analysis efficiency.
This is an MCP server for the Fitbit Web API. It supports OAuth PKCE, a local SQLite cache, and trend analysis.
Based on the description, you at least need access to the Fitbit Web API and authorization through OAuth PKCE. For exact setup details, see the source repository.
This tool adds an MCP interface, local SQLite caching, and trend analysis on top of the Fitbit Web API. For any additional abstractions or features, see the source repository.
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