Pierre Fitness Platform MCP Server

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, Nutrition Methodology, and Mobility 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

• 53 MCP Tools: Activities, goals, analysis, sleep, recovery, nutrition, recipes, mobility, 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

Build with specific providers:

See Build Configuration for provider architecture details.

Modular Architecture

Pierre uses compile-time feature flags for modular deployments. Build only what you need.

Server Profiles

Pre-configured bundles for common deployment scenarios:

Feature Categories

See Build Configuration for detailed feature documentation.

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 53 available MCP tools.

Quick Start

This single command:

• Resets database with fresh migrations

• Seeds admin, AI coaches, demo users, test data, mobility data

• Starts Pierre server (8081), web frontend (3000), Expo mobile (8082)

• Displays all credentials, tokens, and log file paths

See Getting Started for detailed setup.

MCP Client Configuration

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

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

Available MCP Tools

53 tools organized in 9 categories:

Full tool reference: Tools Reference

Server Management

The full setup script does everything:

1. Resets database with fresh migrations

2. Seeds admin user, AI coaches, demo users, test data, mobility data

3. Starts Pierre server, web frontend, and Expo mobile

4. Displays all credentials, tokens, and log file paths

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

First Admin Setup

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

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:

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:

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

• Mobility - stretching and yoga sequences

Testing

See Testing Documentation.

Development Workflow

Before Committing

Before Pushing

The pre-push hook blocks pushes without a valid marker. This decouples test execution from the push to avoid SSH timeout issues.

Contributing

See Contributing Guide.

License

Dual-licensed under Apache 2.0 or MIT.