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partymola/fitbit-mcp

by Various

MCP server for the Fitbit Web API with OAuth PKCE, local SQLite cache, and trend analysis

P

MCP

partymola/fitbit-mcp

Added 1 June 2026

#claude #fitbit #fitbit-api #mcp #mcp-server #model-context-protocol #oauth #quantified-self

Overview

An MCP server that provides access to the Fitbit Web API using OAuth PKCE. It includes a local SQLite cache for data storage and trend analysis capabilities. Built in Python, it allows developers to query Fitbit data through the Model Context Protocol.

Best for

Best for
Developers building MCP-based tools that need to query Fitbit health data.

Use cases

  • Integrate Fitbit data into MCP-compatible clients
  • Cache and analyze personal fitness trends offline
  • Securely authenticate with Fitbit's API using PKCE

Notes

An MCP server that provides access to the Fitbit Web API using OAuth PKCE. It includes a local SQLite cache for data storage and trend analysis capabilities. Built in Python, it allows developers to query Fitbit data through the Model Context Protocol.

0 stars on GitHub. Last updated 2026-05-28. Licensed GPL-3.0.

Use cases

  • Integrate Fitbit data into MCP-compatible clients
  • Cache and analyze personal fitness trends offline
  • Securely authenticate with Fitbit’s API using PKCE

Pros

  • Uses OAuth PKCE for secure authorization without a client secret
  • Local SQLite cache reduces API calls and enables offline access
  • Provides built-in trend analysis for common metrics

Cons

  • Zero stars suggests limited community adoption or testing
  • Requires running a Python server and configuring an MCP client
  • Trend analysis scope may be limited to basic calculations

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Uses OAuth PKCE for secure authorization without a client secret
  • Local SQLite cache reduces API calls and enables offline access
  • Provides built-in trend analysis for common metrics

Cons

  • Zero stars suggests limited community adoption or testing
  • Requires running a Python server and configuring an MCP client
  • Trend analysis scope may be limited to basic calculations