trainingpeaks-mcp-server

ogerbron/trainingpeaks-mcp-server

3.2

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The TrainingPeaks MCP Server is a Model Context Protocol server that facilitates interaction with the TrainingPeaks API, allowing for the retrieval of athlete data, workouts, metrics, and calendar events.

Tools

  1. get_athlete_profile

    Get athlete's profile and training zones.

  2. get_workouts

    Retrieve workouts with optional date filtering.

  3. get_workout_details

    Get detailed information about a specific workout.

  4. get_calendar_events

    Access calendar events within a date range.

  5. get_metrics

    Get health metrics (weight, HRV, steps, stress, sleep).

  6. get_planned_workouts

    Retrieve upcoming planned workouts.

  7. set_auth_tokens

    Set OAuth tokens for API authentication.

TrainingPeaks MCP Server

An MCP (Model Context Protocol) server that enables Claude to interact with the TrainingPeaks API for retrieving athlete data, workouts, metrics, and calendar events.

Features

  • Athlete Profile: Get basic profile information and training zones
  • Workouts: Retrieve workout history and detailed workout information
  • Calendar Events: Access TrainingPeaks calendar events
  • Metrics: Get health and fitness metrics (weight, HRV, steps, stress, sleep)
  • Planned Workouts: Retrieve upcoming planned workouts (up to 7 days)
  • OAuth 2.0 Authentication: Secure authentication with TrainingPeaks API

Prerequisites

  • Python 3.8+
  • TrainingPeaks API credentials (client_id, client_secret)
  • API access approval from TrainingPeaks (required for production use)

Installation

  1. Clone the repository:
git clone <repository-url>
cd trainingpeaks-mcp-server
  1. Install dependencies:
pip install -e .
  1. Copy environment configuration:
cp .env.example .env
  1. Edit .env with your TrainingPeaks API credentials:
TRAININGPEAKS_CLIENT_ID=your_client_id_here
TRAININGPEAKS_CLIENT_SECRET=your_client_secret_here
TRAININGPEAKS_REDIRECT_URI=http://localhost:8080/callback
TRAININGPEAKS_SCOPES=athlete:profile,athlete:workouts
TRAININGPEAKS_ENVIRONMENT=sandbox

Usage

Running the MCP Server

trainingpeaks-mcp-server

Or alternatively:

python -m trainingpeaks_mcp_server.server

To test that the server works:

python test_server.py

Available Tools

  1. get_athlete_profile: Get athlete's profile and training zones
  2. get_workouts: Retrieve workouts with optional date filtering
  3. get_workout_details: Get detailed information about a specific workout
  4. get_calendar_events: Access calendar events within a date range
  5. get_metrics: Get health metrics (weight, HRV, steps, stress, sleep)
  6. get_planned_workouts: Retrieve upcoming planned workouts
  7. set_auth_tokens: Set OAuth tokens for API authentication

Authentication

Before using the API tools, you need to authenticate:

  1. Use TrainingPeaks OAuth flow to obtain access and refresh tokens
  2. Use the set_auth_tokens tool to provide tokens to the MCP server
  3. The server will automatically handle token refresh when needed

API Access

Important: TrainingPeaks API access is currently limited to approved commercial applications. To request access:

  1. Visit https://api.trainingpeaks.com/request-access
  2. Provide information about your application and use case
  3. Allow 7-10 days for response

For development/testing, use the sandbox environment.

Configuration

The server supports both production and sandbox environments:

  • Sandbox: TRAININGPEAKS_ENVIRONMENT=sandbox
  • Production: TRAININGPEAKS_ENVIRONMENT=production

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Format code:

black src/
ruff check src/

License

MIT License - see LICENSE file for details.