Pierre MCP Server Tutorial

Welcome to the comprehensive tutorial for building production-grade MCP (Model Context Protocol) servers in Rust.

What You’ll Learn

This tutorial takes you through every aspect of the Pierre Fitness Intelligence platform, a real-world production MCP server implementation. You’ll learn:

• Production Rust Architecture - Module organization, error handling, configuration management
• Security & Authentication - JWT tokens, cryptographic key management, multi-tenant isolation
• Protocol Implementation - JSON-RPC 2.0, MCP protocol, A2A (Agent-to-Agent) communication
• OAuth 2.0 - Both server and client implementations with PKCE support
• Provider Integration - Pluggable provider architecture for fitness APIs (Strava, Garmin, Fitbit, etc.)
• Sports Science - Real algorithms for TSS, VDOT, FTP, CTL/ATL/TSB
• Testing - Comprehensive testing patterns for async Rust applications

Prerequisites

Before starting this tutorial, you should have:

• Basic Rust knowledge (ownership, borrowing, traits, async/await)
• Familiarity with cargo and Rust project structure
• Understanding of HTTP APIs and JSON
• Basic knowledge of OAuth 2.0 (helpful but not required)

How to Use This Tutorial

Each chapter builds on previous ones but can also be read standalone for reference:

1. Chapters 1-4: Start here for foundational concepts
2. Chapters 5-8: Security-focused deep dive
3. Chapters 9-14: Protocol implementation details
4. Chapters 15-18: OAuth and provider integration
5. Chapters 19-22: Domain-specific implementation
6. Chapters 23-31: Operations and advanced topics

Each chapter includes:

• Prerequisites - Prior knowledge needed
• Code Examples - Real code from the Pierre codebase
• Rust Idioms - Explanations of patterns and best practices

About Pierre

Pierre is a fitness intelligence platform that provides:

• 47 MCP tools for fitness data analysis
• Multi-tenant SaaS architecture with complete data isolation
• OAuth 2.0 server for MCP client authentication
• Pluggable provider system supporting Strava, Garmin, Fitbit, WHOOP, COROS, and Terra
• Sports science algorithms for training load, performance, and recovery analysis
• Local LLM support via OpenAI-compatible APIs (Ollama, vLLM, LocalAI)

The codebase demonstrates production-grade Rust patterns:

• Zero unsafe code policy (with one approved FFI exception)
• Structured error handling (no anyhow! in production)
• Comprehensive test coverage (~650 tests)
• ~330 source files in a well-organized module hierarchy

Getting Started

Let’s begin with Chapter 1: Project Architecture to understand how the codebase is organized.

This tutorial is maintained alongside the Pierre MCP Server codebase.