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.
🏃 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?"
🚀 Want to test the tools right away?
-
- Create an app with OAuth 2.0 Application Type:
Personal - Set Callback URL:
http://localhost:3000/callback - Note your Client ID and Client Secret
- Create an app with OAuth 2.0 Application Type:
-
Install the package globally:
npm install -g mcp-fitbit- 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
- Restart Claude Desktop and ask about your Fitbit data!
- Get Fitbit API credentials (see Installation below)
- Then run:
git clone https://github.com/TheDigitalNinja/mcp-fitbit
cd mcp-fitbit
npm install
# Create .env with your Fitbit credentials
npm run devBoth options open the MCP Inspector at http://localhost:5173 where you can test all tools interactively and handle the OAuth flow.
-
Get Fitbit API credentials at dev.fitbit.com
- Set OAuth 2.0 Application Type to
Personal - Set Callback URL to
http://localhost:3000/callback
- Set OAuth 2.0 Application Type to
-
Install the package:
npm install -g mcp-fitbit
-
Create
.envfile in the package directory:When you run
mcp-fitbitfor the first time, it will tell you exactly where to create the.envfile. It will look something like:C:\Users\YourName\AppData\Roaming\npm\node_modules\mcp-fitbit\.env -
Add your credentials to the
.envfile:FITBIT_CLIENT_ID=your_client_id_here FITBIT_CLIENT_SECRET=your_client_secret_here
-
Run the server:
mcp-fitbit
-
Get Fitbit API credentials at dev.fitbit.com
- Set OAuth 2.0 Application Type to
Personal - Set Callback URL to
http://localhost:3000/callback
- Set OAuth 2.0 Application Type to
-
Clone and setup:
git clone https://github.com/TheDigitalNinja/mcp-fitbit cd mcp-fitbit npm install -
Create
.envfile:FITBIT_CLIENT_ID=your_client_id_here FITBIT_CLIENT_SECRET=your_client_secret_here
-
Build the server:
npm run build
| 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
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
When you first ask your AI assistant to use Fitbit data:
- The server opens your browser to
http://localhost:3000/auth - Log in to Fitbit and grant permissions
- You'll be redirected to a success page
- Your AI can now access your Fitbit data!
npm run lint # Check code quality
npm run format # Fix formatting
npm run build # Compile TypeScript
npm run dev # Run with MCP inspectorArchitecture: See TASKS.md for improvement opportunities and technical details.