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Fitbit MCP Server

by TheDigitalNinja
npmGitHub
TypeScript
MIT License
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Fitbit MCP Connector for AI

Fitbit API CI Coverage Status License: MIT npm version npm downloads

Connect AI assistants to your Fitbit health data

Give your AI assistant access to your Fitbit data for personalized health insights, trend analysis, and automated tracking. Works with Claude Desktop and other MCP-compatible AI tools.

What it does

🏃 Exercise & Activities - Get detailed workout logs and activity data
😴 Sleep Analysis - Retrieve sleep patterns and quality metrics
⚖️ Weight Tracking - Access weight trends over time
❤️ Heart Rate Data - Monitor heart rate patterns and zones
🍎 Nutrition Logs - Review food intake, calories, and macros
👤 Profile Info - Access basic Fitbit profile details

Ask your AI things like: "Show me my sleep patterns this week" or "What's my average heart rate during workouts?"

Quick Start

🚀 Want to test the tools right away?

Option 1: Install from npm (Recommended)

  1. Get Fitbit API credentials

    • Create an app with OAuth 2.0 Application Type: Personal

    • Set Callback URL: http://localhost:3000/callback

    • Note your Client ID and Client Secret

  2. Install the package globally:

npm install -g mcp-fitbit

  1. Add to your Claude Desktop config file:

{ "mcpServers": { "fitbit": { "command": "mcp-fitbit", "args": [], "env": { "FITBIT_CLIENT_ID": "your_client_id_here", "FITBIT_CLIENT_SECRET": "your_client_secret_here" } } } }

  • Config file location:

    • Windows: %AppData%\Claude\claude_desktop_config.json

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Linux: ~/.config/Claude/claude_desktop_config.json

  1. Restart Claude Desktop and ask about your Fitbit data!

Option 2: Development Setup

  1. Get Fitbit API credentials (see Installation below)

  2. Then run:

git clone https://github.com/TheDigitalNinja/mcp-fitbit cd mcp-fitbit npm install # Create .env with your Fitbit credentials npm run dev

Both options open the MCP Inspector at http://localhost:5173 where you can test all tools interactively and handle the OAuth flow.

Installation

For End Users (npm package)

  1. Get Fitbit API credentials at dev.fitbit.com

    • Set OAuth 2.0 Application Type to Personal

    • Set Callback URL to http://localhost:3000/callback

  2. Install the package:

    npm install -g mcp-fitbit

  3. Create

    When you run mcp-fitbit for the first time, it will tell you exactly where to create the .env file. It will look something like:

    C:\Users\YourName\AppData\Roaming\npm\node_modules\mcp-fitbit\.env

  4. Add your credentials to the

    FITBIT_CLIENT_ID=your_client_id_here FITBIT_CLIENT_SECRET=your_client_secret_here

  5. Run the server:

    mcp-fitbit

For Developers (from source)

  1. Get Fitbit API credentials at dev.fitbit.com

    • Set OAuth 2.0 Application Type to Personal

    • Set Callback URL to http://localhost:3000/callback

  2. Clone and setup:

    git clone https://github.com/TheDigitalNinja/mcp-fitbit cd mcp-fitbit npm install

  3. Create

    FITBIT_CLIENT_ID=your_client_id_here FITBIT_CLIENT_SECRET=your_client_secret_here

  4. Build the server:

    npm run build

Available Tools

Tool

Description

Parameters

get_weight

Weight data over time periods

period

:

1d

,

7d

,

30d

,

3m

,

6m

,

1y

get_sleep_by_date_range

Sleep logs for date range (max 100 days)

startDate

,

endDate

(YYYY-MM-DD)

get_exercises

Activity/exercise logs after date

afterDate

(YYYY-MM-DD),

limit

(1-100)

get_daily_activity_summary

Daily activity summary with goals

date

(YYYY-MM-DD)

get_activity_goals

User's activity goals (daily/weekly)

period

:

daily

,

weekly

get_activity_timeseries

Activity time series data (max 30 days)

resourcePath

,

startDate

,

endDate

(YYYY-MM-DD)

get_azm_timeseries

Active Zone Minutes time series (max 1095 days)

startDate

,

endDate

(YYYY-MM-DD)

get_heart_rate

Heart rate for time period

period

:

1d

,

7d

,

30d

,

1w

,

1m

, optional

date

get_heart_rate_by_date_range

Heart rate for date range (max 1 year)

startDate

,

endDate

(YYYY-MM-DD)

get_food_log

Complete nutrition data for a day

date

(YYYY-MM-DD or "today")

get_nutrition

Individual nutrient over time

resource

,

period

, optional

date

get_nutrition_by_date_range

Individual nutrient for date range

resource

,

startDate

,

endDate

get_profile

User profile information

None

Nutrition resources: caloriesIn, water, protein, carbs, fat, fiber, sodium

Activity time series resources: steps, distance, calories, activityCalories, caloriesBMR, tracker/activityCalories, tracker/calories, tracker/distance

Claude Desktop

Using npm package (recommended):

Add to claude_desktop_config.json:

{ "mcpServers": { "fitbit": { "command": "mcp-fitbit", "args": [] } } }

Using local development version:

Add to claude_desktop_config.json:

{ "mcpServers": { "fitbit": { "command": "node", "args": ["C:\\path\\to\\mcp-fitbit\\build\\index.js"] } } }

Config file locations:

  • Windows: %AppData%\Claude\claude_desktop_config.json

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Linux: ~/.config/Claude/claude_desktop_config.json

First Run Authorization

When you first ask your AI assistant to use Fitbit data:

  1. The server opens your browser to http://localhost:3000/auth

  2. Log in to Fitbit and grant permissions

  3. You'll be redirected to a success page

  4. Your AI can now access your Fitbit data!

Development

npm run lint # Check code quality npm run format # Fix formatting npm run build # Compile TypeScript npm run dev # Run with MCP inspector

Architecture: See TASKS.md for improvement opportunities and technical details.

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Tools

A Model Context Protocol server that bridges LLMs (like Claude) with the Fitbit API, allowing the LLM to request and retrieve user health and fitness data through defined tools.

  1. What it does
    1. Quick Start
      1. Option 1: Install from npm (Recommended)
      2. Option 2: Development Setup
    2. Installation
      1. For End Users (npm package)
      2. For Developers (from source)
    3. Available Tools
      1. Claude Desktop
      2. First Run Authorization
    4. Development

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