TrainingPeaks MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@TrainingPeaks MCP ServerShow me my CTL/ATL/TSB and suggest a workout for today."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
TrainingPeaks MCP Server
MCP server for TrainingPeaks. Reads workouts, fitness metrics (CTL/ATL/TSB), and health data. Creates, updates, and deletes planned workouts. Built with Python/FastMCP with browser-based auth and auto-refresh. Designed for AI-powered training plan management and automated adjustment based on performance, fatigue, and injury.
Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -e .Related MCP server: claude-garmin
Usage
python -m training_peaks_mcpOn first run, a browser window opens with the TrainingPeaks login page. Enter your credentials — they're sent to localhost only, saved locally to ~/.config/training-peaks-mcp/credentials.json, and auto-refresh when expired.
CLI Options
python -m training_peaks_mcp # Start the MCP server
python -m training_peaks_mcp --auth # Re-authenticate
python -m training_peaks_mcp --auth-status # Check auth status
python -m training_peaks_mcp --auth-clear # Remove stored credentialsTools
Auth
Tool | Description |
| Login with username/password via MCP |
Read
Tool | Description |
| Athlete profile (ID, name, account type) |
| Workouts by date range (filter: all/planned/completed) |
| Single workout with full details and structure |
| CTL/ATL/TSB from the Performance Management Chart |
| Health metrics (weight, HRV, sleep, SpO2, steps) |
| Power, heart rate, and speed/pace zones |
| Nutrition data by date range |
Write
Tool | Description |
| Create a planned workout |
| Update an existing workout |
| Delete a workout |
| Log health metrics (weight, HRV, sleep, injury, etc.) |
| Add a comment to a workout |
Authentication
Three methods, checked in order:
Stored credentials — saved automatically after first login, auto-refreshes on expiry
Browser login — opens on first run if no credentials exist
Environment variable — set
TP_AUTH_COOKIEto override (for CI/containers)
Credentials are stored at ~/.config/training-peaks-mcp/credentials.json with 0600 permissions.
Architecture
src/training_peaks_mcp/
├── __init__.py
├── __main__.py # CLI entry point
├── auth.py # Login flow, credential storage, browser auth page
├── client.py # Async HTTP client for tpapi.trainingpeaks.com
└── server.py # FastMCP tool definitionsThe server uses the TrainingPeaks internal API (tpapi.trainingpeaks.com). Auth cookies are exchanged for short-lived bearer tokens via /users/v3/token.
Supported Sports
Swim, Bike, Run, Brick, Crosstrain, Race, DayOff, MtnBike, Strength, Custom, XCSki, Rowing, Walk, Other
License
MIT
This server cannot be deployed
Maintenance
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