TrainingPeaks MCP Server

Connect TrainingPeaks to Claude and other AI assistants via the Model Context Protocol (MCP). Query workouts, build structured intervals, manage your calendar, track fitness trends, and control your training through natural conversation.

No API approval required. The official Training Peaks API is approval-gated, but this server uses secure cookie authentication that any user can set up in minutes. Your cookie is stored in your system keyring, never transmitted anywhere except to TrainingPeaks.

What You Can Do

Ask your AI assistant things like:

• "Build me a 4x8min threshold session for Tuesday with warm-up and cool-down"
• "Schedule my mobility session for April 14, 2026 at 16:45"
• "Compare my FTP progression this year vs last year"
• "Copy last week's long ride to this Saturday"
• "Log my weight at 74.5kg and sleep at 7.5 hours"
• "What's my weekly TSS so far? Am I on track for my ATP target?"
• "Show my race calendar and how many weeks until my A race"
• "Set my FTP to 310 and update my power zones"
• "Add a calendar note for next Monday: rest day, travel"

Tools (64)

Workouts

Analysis & Performance

Athlete Settings

Health Metrics

Equipment

Events & Calendar

Workout Library

Reference & Auth

Setup Options

Option A: Auto-Setup with Claude Code

If you have Claude Code, paste this prompt:

Set up the TrainingPeaks MCP server from https://github.com/JamsusMaximus/trainingpeaks-mcp - clone it, create a venv, install it, then walk me through getting my TrainingPeaks cookie from my browser and run tp-mcp auth. Finally, add it to my Claude Desktop config.

Claude will handle the installation and guide you through authentication step-by-step.

Option B: Manual Setup

Step 1: Install

git clone https://github.com/JamsusMaximus/trainingpeaks-mcp.git
cd trainingpeaks-mcp
python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e .

Step 2: Authenticate

Option A: Auto-extract from browser (easiest)

If you're logged into TrainingPeaks in your browser:

pip install tp-mcp[browser]  # One-time: install browser support
tp-mcp auth --from-browser chrome  # Or: firefox, safari, edge, auto

macOS note: You may see security prompts for Keychain or Full Disk Access. This is normal - browser cookies are encrypted and require permission to read.

Option B: Manual cookie entry

1. Log into app.trainingpeaks.com
2. Open DevTools (F12) -> Application tab -> Cookies
3. Find Production_tpAuth and copy its value
4. Run tp-mcp auth and paste when prompted

Other auth commands:

tp-mcp auth-status  # Check if authenticated
tp-mcp auth-clear   # Remove stored cookie

Step 3: Add to Claude Desktop

Run this to get your config snippet:

tp-mcp config

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows) and paste it inside mcpServers. Example with multiple servers:

{
  "mcpServers": {
    "some-other-server": {
      "command": "npx",
      "args": ["some-other-mcp"]
    },
    "trainingpeaks": {
      "command": "/Users/you/trainingpeaks-mcp/.venv/bin/tp-mcp",
      "args": ["serve"]
    }
  }
}

Restart Claude Desktop. You're ready to go!

Structured Workouts

Create workouts with full interval structure. The server auto-computes duration, IF, and TSS from the structure:

{
  "date": "2026-03-01",
  "sport": "Bike",
  "title": "Sweet Spot Intervals",
  "structure": {
    "primaryIntensityMetric": "percentOfFtp",
    "steps": [
      {"name": "Warm Up", "duration_seconds": 600, "intensity_min": 40, "intensity_max": 55, "intensityClass": "warmUp"},
      {"type": "repetition", "reps": 4, "steps": [
        {"name": "Sweet Spot", "duration_seconds": 480, "intensity_min": 88, "intensity_max": 93, "intensityClass": "active"},
        {"name": "Recovery", "duration_seconds": 120, "intensity_min": 50, "intensity_max": 60, "intensityClass": "rest"}
      ]},
      {"name": "Cool Down", "duration_seconds": 600, "intensity_min": 40, "intensity_max": 55, "intensityClass": "coolDown"}
    ]
  }
}

The LLM builds this JSON naturally from conversation - just say "build me 4x8min sweet spot with 2min rest".

You can use the same simplified structure object with tp_update_workout:

{
  "workout_id": "3658666303",
  "duration_minutes": 57,
  "tss_planned": 62.3,
  "structure": {
    "primaryIntensityMetric": "percentOfThresholdHr",
    "steps": [
      {"name": "Warm-up", "duration_seconds": 900, "intensity_min": 65, "intensity_max": 80, "intensityClass": "warmUp"},
      {"type": "repetition", "name": "4x5min controlled tempo", "reps": 4, "steps": [
        {"name": "Interval", "duration_seconds": 300, "intensity_min": 89, "intensity_max": 94, "intensityClass": "active"},
        {"name": "Jog recovery", "duration_seconds": 180, "intensity_min": 65, "intensity_max": 83, "intensityClass