Pierre Fitness API

Active Development - This project is under active development and not yet ready for production use. We welcome feedback, contributions, and early testing from the community. Please report issues and share your experiences.

An open source multi-protocol fitness data API providing secure access to fitness data from multiple providers (Strava, Fitbit) through the Model Context Protocol (MCP), A2A (Agent-to-Agent) Protocol, and REST APIs. Built for LLMs and AI applications with comprehensive API key management, rate limiting, OAuth integration, and real-time analytics.

Fitness Data Analysis

Pierre connects fitness data to AI assistants like Claude, ChatGPT, and any agents supporting the MCP/A2A protocol, providing intelligent analysis with location, weather, and performance context.

Analysis Type	Example Queries	Key Features
Running	"What was my longest run this year and where?"	Pace trends, location context, terrain analysis
Cross-Training	"Compare my cycling vs running performance"	Multi-sport analysis, heart rate zones, consistency
Location Intelligence	"Where do I perform best?"	GPS tracking, terrain impact, route optimization
Weather Impact	"How does weather affect my performance?"	Temperature correlation, seasonal patterns
Activity Intelligence	"Analyze my marathon with full context"	Insights with environmental factors
Goal Tracking	"How close am I to my 1000km goal?"	Progress monitoring, achievement analysis
Performance Trends	"Find patterns in my training data"	Long-term analysis, optimization suggestions

See detailed examples: Check out our comprehensive prompt guide with 50+ real-world queries.

Integration Methods

Pierre supports multiple integration patterns for different use cases:

Integration Type	Best For	Setup Complexity	Authentication
MCP Protocol (2025-06-18)	AI assistants (Claude, ChatGPT, any MCP compliant client)	Low	JWT Token
A2A Protocol (v0.2.3)	AI agents & applications	Medium	API Keys
REST API	Web apps & dashboards	Medium	OAuth2 + JWT
Single-Tenant	Personal local use	Minimal	Optional

Quick Setup Examples

AI Assistant Integration (Claude, ChatGPT, etc.)

1. Configure MCP Server

   // For Claude Desktop (~/.claude/claude_desktop_config.json)
   {
     "mcpServers": {
       "pierre-fitness": {
         "command": "path/to/pierre-mcp-server",
         "args": ["--single-tenant", "--port", "8080"]
       }
     }
   }
   
   // For ChatGPT or other MCP-compatible clients
   // Use the same MCP protocol with your client's configuration

2. Connect to Strava

   • Visit the OAuth URL provided by Pierre
   • Authorize access to your Strava data
   • Start asking questions in natural language

3. Works with any MCP/A2A compatible agent

   • Claude Desktop, ChatGPT with MCP support
   • Custom AI agents, GitHub Copilot extensions
   • Any application supporting MCP or A2A protocols

A2A Integration for Developers

# 1. Register your A2A client
curl -X POST http://localhost:8081/a2a/clients \
  -H "Content-Type: application/json" \
  -d '{
    "name": "My Fitness App",
    "description": "AI fitness coach",
    "capabilities": ["fitness-data-analysis"],
    "contact_email": "developer@myapp.com"
  }'

# 2. Authenticate client
curl -X POST http://localhost:8081/a2a/auth \
  -H "Content-Type: application/json" \
  -d '{
    "client_id": "your_client_id",
    "client_secret": "your_client_secret",
    "scopes": ["read", "write"]
  }'

# 3. Execute fitness tools (requires user JWT token)
curl -X POST http://localhost:8081/a2a/execute \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer USER_JWT_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools.execute",
    "params": {
      "tool_name": "get_activities",
      "parameters": {"provider": "strava", "limit": 10}
    },
    "id": 1
  }'

📚 A2A Quick Start: See A2A_QUICK_START.md for complete 5-minute setup guide.

Detailed guides: See our setup documentation for complete integration examples.

Documentation

Guide	Description	Key Topics
Documentation Index	Complete doc guide	All documentation organized by use case
Getting Started	Setup and configuration	Local setup, OAuth config, Docker deployment
API Reference	Complete API documentation	MCP tools, A2A protocol, HTTP endpoints
A2A Quick Start	5-minute A2A setup	Enterprise integration, client registration
A2A Reference	Complete A2A guide	Authentication, tools, Python client
OpenAPI Specification	Interactive API reference	Complete API spec with examples
Database Guide	Database setup	SQLite/PostgreSQL setup and database plugins
Deployment Guide	Production deployment	Docker, Kubernetes, cloud platforms

Key Features

Category	Features
Integrations	Strava, Fitbit • MCP Protocol • A2A Protocol • REST APIs
Security	OAuth2 + PKCE • JWT Authentication • Encrypted storage • Rate limiting
Intelligence	Activity analysis • Location detection • Weather integration
Architecture	Multi-tenant • Cloud-ready • Extensible • Production-ready

Quick Start

Method	Command	Use Case
Local	cargo run --bin pierre-mcp-server	Development, local testing
Docker	./docker-compose-with-envrc.sh up	Easy deployment, cloud-ready
AI Assistants	Add to MCP client config	Claude, ChatGPT, agent integration

One-Minute Setup

# 1. Clone and build
git clone https://github.com/Async-IO/pierre_mcp_server.git
cd pierre_mcp_server && cargo build --release

# 2. Set up environment and run server
# Configure your environment variables (see docs/GETTING_STARTED.md)
cargo run --bin pierre-mcp-server

# 3. Configure AI Assistant (Claude, ChatGPT, etc.)
echo '{
  "mcpServers": {
    "pierre-fitness": {
      "command": "'$(pwd)'/target/release/pierre-mcp-server",
      "args": ["--mcp-port", "8080"]
    }
  }
}' > ~/.claude/claude_desktop_config.json

Fresh Start: Need to clean your database? See our Database Cleanup Guide.

Architecture

Pierre MCP Server is built as a production-ready, multi-tenant application with comprehensive security and scalability features:

• Authentication: JWT-based user authentication with secure token management
• Database: Encrypted storage with SQLite/PostgreSQL support
• Security: User isolation, rate limiting, and encrypted data storage
• Scalability: Cloud-ready deployment with Docker and Kubernetes support

License

This project is dual-licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT License (LICENSE-MIT)

at your option.

Contributing

We welcome contributions! See CONTRIBUTING.md for details on how to get started.