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Cadence

AI-powered coaching platform that connects to your fitness wearables and provides personalized training insights, recovery monitoring, and AI-driven coaching conversations.

What It Does

Cadence pulls data from your fitness devices and uses AI to help you train smarter:

  • Daily briefings — sleep quality, recovery status, training readiness
  • Training analytics — load tracking, zone analysis, performance trends, injury risk detection
  • AI coaching chat — ask questions about your training, get personalized advice backed by your data
  • Workout logging — track strength training sessions with progressive overload tracking
  • Multi-device support — connect Garmin, WHOOP, and Strava simultaneously

How It Works

Cadence is built on the Model Context Protocol (MCP). It exposes 75 tools that AI assistants can call to fetch your fitness data, analyze trends, and log workouts. It also ships with a built-in web dashboard and chat interface.

┌─────────────┐     ┌─────────────────┐     ┌──────────────┐
│ Claude       │     │                 │     │ Garmin       │
│ Desktop      │────▶│    Cadence      │────▶│ WHOOP        │
│ or Web UI    │◀────│  (MCP Server)   │◀────│ Strava       │
└─────────────┘     └─────────────────┘     └──────────────┘

Quick Start

git clone https://github.com/ladkam/coaching-mcp.git
cd coaching-mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env   # then fill in your credentials
python server.py --http

See the Getting Started guide for full setup instructions.

Key Sections

Section What You'll Find
Getting Started Installation, first run, connecting devices
Configuration Environment variables, feature flags
Architecture System design, database schema, project structure
Integrations Device-specific setup and data reference
MCP Tools Complete tool reference (75 tools)
AI Chat LLM provider setup, coaching behavior
REST API HTTP endpoints for building clients
Deployment Railway, Heroku, production config