Content
# TrainingPeaks MCP Server
<a href="https://glama.ai/mcp/servers/@JamsusMaximus/TrainingPeaks-MCP">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@JamsusMaximus/TrainingPeaks-MCP/badge" alt="TrainingPeaks MCP server" />
</a>
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
| 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, attach workouts as legs (multisport) |
| `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](https://claude.ai/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
```bash
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:
```bash
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](https://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:**
```bash
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:
```bash
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:
```json
{
"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:
```json
{
"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`:
```json
{
"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`.
```json
{
"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:
```json
{
"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](https://modelcontextprotocol.io) 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.
### Cookie Storage
| 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.
### Cookie Never Leaks to AI
The AI assistant (Claude) **never sees your cookie value**. Multiple layers ensure this:
1. **Return value sanitisation**: Tool results are scrubbed for any keys containing `cookie`, `token`, `auth`, `credential`, `password`, or `secret` before being sent to Claude
2. **Masked repr()**: The `BrowserCookieResult` and `CredentialResult` classes override `__repr__` to show `cookie=<present>` instead of the actual value
3. **Sanitised exceptions**: Error messages use only exception type names, never full messages that could contain data
4. **No logging**: Cookie values are never written to any log
### Domain Hardcoding (Cannot Be Changed)
The browser cookie extraction **only** accesses `.trainingpeaks.com`:
```python
# 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`](src/tp_mcp/auth/browser.py) - Cookie extraction with hardcoded domain
- [`src/tp_mcp/auth/encrypted.py`](src/tp_mcp/auth/encrypted.py) - AES-256-GCM credential encryption
- [`src/tp_mcp/tools/_validation.py`](src/tp_mcp/tools/_validation.py) - Pydantic input validation
- [`src/tp_mcp/tools/refresh_auth.py`](src/tp_mcp/tools/refresh_auth.py) - Result sanitisation
- [`tests/test_tools/test_refresh_auth_security.py`](tests/test_tools/test_refresh_auth_security.py) - Security tests
## Authentication Flow
The server uses a two-step authentication process:
1. **Cookie to OAuth Token**: Your stored cookie is exchanged for a short-lived OAuth access token (expires in 1 hour)
2. **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
```bash
pip install -e ".[dev]"
pytest tests/ -v
mypy src/
ruff check src/
```
## Licence
MIT
