Pierre Fitness Platform MCP Server

<div align="center"> <img src="templates/pierre-logo.svg" width="150" height="150" alt="Pierre Fitness Platform Logo"> <h1>Pierre Fitness Platform</h1> </div>

Pierre Fitness Platform connects AI assistants to fitness data from Strava, Garmin, Fitbit, WHOOP, COROS, and Terra (150+ wearables). Implements Model Context Protocol (MCP), A2A protocol, OAuth 2.0, and REST APIs for Claude, ChatGPT, and other AI assistants.

Intelligence System

Sports science-based fitness analysis including training load management, race predictions, sleep and recovery scoring, nutrition planning, and pattern detection.

See Intelligence Methodology and Nutrition Methodology for details.

Features

• MCP Protocol: JSON-RPC 2.0 for AI assistant integration
• A2A Protocol: Agent-to-agent communication
• OAuth 2.0 Server: RFC 7591 dynamic client registration
• 47 MCP Tools: Activities, goals, analysis, sleep, recovery, nutrition, recipes, configuration
• TypeScript SDK: pierre-mcp-client npm package
• Pluggable Providers: Compile-time provider selection
• TOON Format: Token-Oriented Object Notation output for ~40% LLM token reduction (spec)

Provider Support

Provider	Feature Flag	Capabilities
Strava	provider-strava	Activities, Stats, Routes
Garmin	provider-garmin	Activities, Sleep, Health
WHOOP	provider-whoop	Sleep, Recovery, Strain
Fitbit	provider-fitbit	Activities, Sleep, Health
COROS	provider-coros	Activities, Sleep, Recovery
Terra	provider-terra	150+ wearables, Activities, Sleep, Health
Synthetic	provider-synthetic	Development/Testing

Build with specific providers:

cargo build --release                                                    # all providers
cargo build --release --no-default-features --features "sqlite,provider-strava"  # strava only

See Pluggable Provider Architecture.

What You Can Ask

• "Calculate my daily nutrition needs for marathon training"
• "Analyze my training load - do I need a recovery day?"
• "Compare my three longest runs this month"
• "Analyze this meal: 150g chicken, 200g rice, 100g broccoli"
• "What's my predicted marathon time based on recent runs?"

See Tools Reference for the 47 available MCP tools.

Quick Start

git clone https://github.com/Async-IO/pierre_mcp_server.git
cd pierre_mcp_server
cp .envrc.example .envrc  # edit with your settings
direnv allow              # or: source .envrc
./bin/setup-and-start.sh  # complete setup: fresh DB, admin user, server start

Server starts on http://localhost:8081. See Getting Started for detailed setup.

MCP Client Configuration

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "pierre-fitness": {
      "command": "npx",
      "args": ["-y", "pierre-mcp-client@next", "--server", "http://localhost:8081"]
    }
  }
}

The SDK handles OAuth 2.0 authentication automatically. See SDK Documentation.

Available MCP Tools

47 tools organized in 8 categories:

Category	Tools	Description
Core Fitness	6	Activities, athlete profile, provider connections
Goals	4	Goal setting, suggestions, feasibility, progress
Analysis	10	Metrics, trends, patterns, predictions, recommendations
Sleep & Recovery	5	Sleep quality, recovery score, rest recommendations
Nutrition	5	BMR/TDEE, macros, USDA food search, meal analysis
Recipes	7	Training-aware meal planning and recipe storage
Configuration	6	User settings, training zones, profiles
Fitness Config	4	Fitness parameters, thresholds

Full tool reference: docs/tools-reference.md

Server Management

./bin/setup-and-start.sh  # complete setup: fresh DB, admin user, server start
./bin/start-server.sh     # start backend only (loads .envrc)
./bin/stop-server.sh      # stop backend
./bin/start-frontend.sh   # start dashboard (http://localhost:5173)

Options for setup-and-start.sh:

• --skip-fresh-start - preserve existing database
• --run-tests - run workflow tests after startup
• --admin-email EMAIL - custom admin email
• --admin-password PWD - custom admin password

User Portal Dashboard

Web-based dashboard for users and administrators at http://localhost:5173.

Features

• Role-Based Access: super_admin, admin, user roles with permission hierarchy
• User Registration: Self-registration with admin approval workflow
• API Key Management: Create, view, deactivate API keys
• MCP Tokens: Generate tokens for Claude Desktop and AI assistants
• Usage Analytics: Request patterns, tool usage charts
• Super Admin Impersonation: View dashboard as any user for support

User Roles

Role	Capabilities
User	Own API keys, MCP tokens, analytics
Admin	+ User approval, all users analytics
Super Admin	+ Impersonation, admin tokens, system config

First Admin Setup

cargo run --bin admin-setup -- create-admin-user \
  --email admin@example.com \
  --password SecurePassword123 \
  --super-admin

See Frontend Documentation for detailed dashboard documentation.

Mobile App

React Native mobile app for iOS and Android with conversational AI interface.

Features

• AI Chat Interface: Conversational UI with markdown rendering and real-time streaming
• Fitness Provider Integration: Connect to Strava, Garmin, Fitbit, WHOOP, COROS via OAuth
• Activity Tracking: View and analyze your fitness activities
• Training Insights: Get AI-powered training recommendations

Quick Start

cd frontend-mobile
bun install
bun start   # Start Expo development server
bun run ios # Run on iOS Simulator

See Mobile App README and Mobile Development Guide.

AI Coaches

Pierre includes an AI coaching system with 9 default coaching personas and support for user-created personalized coaches.

Default Coaches

The system includes 9 AI coaching personas across 5 categories:

Category	Icon	Coaches
Training	🏃	Endurance Coach, Speed Coach
Nutrition	🥗	Sports Nutritionist, Hydration Specialist
Recovery	😴	Recovery Specialist, Sleep Coach
Recipes	👨‍🍳	Performance Chef, Meal Prep Expert
Analysis	📊	Data Analyst

Default coaches are seeded automatically by ./bin/setup-and-start.sh and are visible to all users.

Personalized Coaches

Users can create their own AI coaches with custom:

• Name and personality
• System prompts and behavior
• Category assignment
• Avatar customization

User-created coaches appear in a "Personalized" section above system coaches and are private to each user.

Coach Seeder

To seed or refresh the default coaches:

cargo run --bin seed-coaches

This creates the 9 default AI coaching personas if they don't already exist.

Documentation

Reference

• Getting Started - installation, configuration, first run
• Architecture - system design, components, request flow
• Protocols - MCP, OAuth2, A2A, REST
• Authentication - JWT, API keys, OAuth2 flows
• Configuration - environment variables, algorithms

Development

• Development Guide - workflow, dashboard, testing
• Scripts Reference - 30+ development scripts
• CI/CD - GitHub Actions, pipelines
• Release Guide - releasing server and SDK to npm
• Contributing - code standards, PR workflow

Components

• SDK - TypeScript client for MCP integration
• Frontend - React dashboard
• Mobile - React Native mobile app
• Mobile Development - mobile dev setup guide

Methodology

• Intelligence - sports science formulas
• Nutrition - dietary calculations

Testing

cargo test                        # all tests
./scripts/lint-and-test.sh        # full CI suite
./scripts/smoke-test.sh           # quick validation (~3 min)

See Testing Documentation.

Contributing

See Contributing Guide.

License

Dual-licensed under Apache 2.0 or MIT.