TrainingPeaks MCP Server

May 17, 2026 · View on GitHub

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

Example conversation with Claude using TrainingPeaks MCP

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

Tool	Description
`tp_get_workouts`	List workouts in a date range (max 90 days)
`tp_get_workout`	Get full details for a single workout
`tp_create_workout`	Create a workout with optional interval structure, auto-computed IF/TSS, and optional planned start time
`tp_update_workout`	Update any field of an existing workout, including structured intervals and planned start time
`tp_delete_workout`	Delete a workout
`tp_copy_workout`	Copy a workout to a new date (preserves structure and planned fields)
`tp_reorder_workouts`	Reorder workouts on a given day
`tp_pair_workout`	Pair a completed workout with a planned workout (merges into one)
`tp_unpair_workout`	Unpair a workout (splits into separate completed and planned workouts)
`tp_validate_structure`	Validate interval structure without creating a workout
`tp_get_workout_comments`	Get comments on a workout
`tp_add_workout_comment`	Add a comment to a workout
`tp_get_workout_note`	Get the private workout note for a workout
`tp_set_workout_note`	Set or update the private workout note
`tp_upload_workout_file`	Upload a FIT/TCX/GPX file to a workout
`tp_download_workout_file`	Download a workout's device file
`tp_delete_workout_file`	Delete an attached file from a workout

Analysis & Performance

Tool	Description
`tp_analyze_workout`	Detailed analysis with time-series data, zones, and laps
`tp_get_peaks`	Power PRs (5s-90min) and running PRs (400m-marathon)
`tp_get_workout_prs`	PRs set during a specific session
`tp_get_fitness`	CTL, ATL, and TSB trend (fitness, fatigue, form)
`tp_get_weekly_summary`	Combined workouts + fitness for a week with totals
`tp_get_atp`	Annual Training Plan - weekly TSS targets, periods, races

Athlete Settings

Tool	Description
`tp_get_athlete_settings`	Get FTP, thresholds, zones, profile
`tp_update_ftp`	Update FTP and recalculate the default power zones
`tp_update_hr_zones`	Update heart rate zones
`tp_update_speed_zones`	Update run/swim pace zones
`tp_update_nutrition`	Update daily planned calories
`tp_get_pool_length_settings`	Get pool length options

Health Metrics

Tool	Description
`tp_log_metrics`	Log weight, HRV, sleep, steps, SpO2, pulse, RMR, injury
`tp_get_metrics`	Get health metrics for a date range
`tp_get_nutrition`	Get nutrition data for a date range

Equipment

Tool	Description
`tp_get_equipment`	List bikes and shoes with distances
`tp_create_equipment`	Add a bike or shoe
`tp_update_equipment`	Update equipment details, retire
`tp_delete_equipment`	Delete equipment

Events & Calendar

Tool	Description
`tp_get_focus_event`	Get A-priority focus event with goals
`tp_get_next_event`	Get nearest future event
`tp_get_events`	List events in a date range
`tp_create_event`	Add a race/event with priority (A/B/C) and CTL target
`tp_update_event`	Update event details
`tp_delete_event`	Delete an event
`tp_create_note`	Create a calendar note
`tp_get_note`	Get a calendar note by ID
`tp_update_note`	Update title, description, date or visibility of a note
`tp_delete_note`	Delete a calendar note
`tp_get_note_comments`	List all comments on a note
`tp_add_note_comment`	Add a comment to a note
`tp_get_availability`	List unavailable/limited periods
`tp_create_availability`	Mark dates as unavailable or limited
`tp_delete_availability`	Remove availability entry

Workout Library

Tool	Description
`tp_get_libraries`	List workout library folders
`tp_get_library_items`	List templates in a library
`tp_get_library_item`	Get full template details including structure
`tp_create_library`	Create a library folder
`tp_delete_library`	Delete a library folder
`tp_create_library_item`	Save a workout template
`tp_update_library_item`	Edit a template
`tp_schedule_library_workout`	Schedule a template to a calendar date

Reference & Auth

Tool	Description
`tp_get_workout_types`	List all sport types and subtypes with IDs
`tp_get_profile`	Get athlete profile
`tp_auth_status`	Check authentication status
`tp_list_athletes`	List athletes (coach accounts)
`tp_refresh_auth`	Re-authenticate from browser cookie

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

Log into app.trainingpeaks.com
Open DevTools (F12) -> Application tab -> Cookies
Find Production_tpAuth and copy its value
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": "rest"}
      ]},
      {"name": "Cool-down", "duration_seconds": 600, "intensity_min": 65, "intensity_max": 80, "intensityClass": "coolDown"}
    ]
  }
}

If duration_minutes and tss_planned are omitted, they are derived from the structure. If you pass them explicitly, they override the derived values.

For advanced round-trip use cases, tp_create_workout and tp_update_workout also accept a native structured_workout payload in TrainingPeaks builder format. When a workout already has a native structure, tp_get_workout returns it as structured_workout.

Workout comments are exposed via tp_get_workout()["workout_comments"] or tp_get_workout_comments(). The older top-level coach_comments and athlete_comments fields are no longer returned by tp_get_workout.

{
  "workout_id": "3658666303",
  "structured_workout": {
    "structure": [],
    "polyline": [],
    "primaryLengthMetric": "duration",
    "primaryIntensityMetric": "percentOfFtp",
    "primaryIntensityTargetOrRange": "range"
  }
}

Use either structure or structured_workout in a single create/update call, not both.

For planned workout scheduling, tp_create_workout and tp_update_workout accept:

YYYY-MM-DD for all-day planning on a calendar date
YYYY-MM-DDTHH:MM:SS for a planned start time on that date

TrainingPeaks stores planned workout times separately from the calendar day. Internally this means:

workoutDay stays at midnight for the selected date
startTimePlanned stores the planned start time
planned end time is derived from startTimePlanned + totalTimePlanned

Example with a planned start time:

{
  "date": "2026-04-14T16:45:00",
  "sport": "Strength",
  "title": "Core & Mobility",
  "duration_minutes": 60,
  "description": "Core stabilisation and stretching."
}

What is MCP?

Model Context Protocol is an open standard for connecting AI assistants to external data sources. MCP servers expose tools that AI models can call to fetch real-time data, enabling assistants like Claude to access your Training Peaks account through natural language.

Security

TL;DR: Your cookie is encrypted on disk, exchanged for short-lived OAuth tokens, never shown to Claude, and only ever sent to TrainingPeaks. The server has no network ports.

This server is designed with defence-in-depth. Your TrainingPeaks session cookie is sensitive - it grants access to your training data - so we treat it accordingly.

Write access: v2.0 adds full calendar management (create, update, delete workouts, events, notes, equipment, settings). All mutations go through Pydantic validation. The server cannot access billing or payment info.

Platform	Primary Storage	Fallback
macOS	System Keychain	Encrypted file
Windows	Windows Credential Manager	Encrypted file
Linux	Secret Service (GNOME/KDE)	Encrypted file

Your cookie is never stored in plaintext. The encrypted file fallback uses AES-256-GCM authenticated encryption with a PBKDF2-derived key (600,000 iterations) and a machine-specific salt.

The AI assistant (Claude) never sees your cookie value. Multiple layers ensure this:

Return value sanitisation: Tool results are scrubbed for any keys containing cookie, token, auth, credential, password, or secret before being sent to Claude
Masked repr(): The BrowserCookieResult and CredentialResult classes override __repr__ to show cookie=<present> instead of the actual value
Sanitised exceptions: Error messages use only exception type names, never full messages that could contain data
No logging: Cookie values are never written to any log

Domain Hardcoding (Cannot Be Changed)

The browser cookie extraction only accesses .trainingpeaks.com:

# From src/tp_mcp/auth/browser.py - HARDCODED, not a parameter
cj = func(domain_name=".trainingpeaks.com")

Claude cannot modify this via tool parameters. The only parameter is browser (chrome/firefox/etc), not the domain. To change the domain would require modifying the source code.

No Network Exposure

The MCP server uses stdio transport only - it communicates with Claude Desktop via stdin/stdout, not over the network. There is no HTTP server, no open ports, no remote access.

Open Source

This server is fully open source. You can audit every line of code before running it. Key security files:

src/tp_mcp/auth/browser.py - Cookie extraction with hardcoded domain
src/tp_mcp/auth/encrypted.py - AES-256-GCM credential encryption
src/tp_mcp/tools/_validation.py - Pydantic input validation
src/tp_mcp/tools/refresh_auth.py - Result sanitisation
tests/test_tools/test_refresh_auth_security.py - Security tests

Authentication Flow

The server uses a two-step authentication process:

Cookie to OAuth Token: Your stored cookie is exchanged for a short-lived OAuth access token (expires in 1 hour)
Automatic Refresh: Tokens are cached in memory and automatically refreshed before expiry

This means:

You only need to authenticate once with tp-mcp auth
API calls use proper Bearer token auth, not cookies
If your session cookie expires (typically after several weeks), use tp_refresh_auth in Claude or run tp-mcp auth again

Development

pip install -e ".[dev]"
pytest tests/ -v
mypy src/
ruff check src/

Licence

MIT