Pierre Fitness Platform

Pierre Fitness Platform connects AI assistants to fitness data from Strava, Garmin, Fitbit, and WHOOP. 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:

• Training Load: TSS, CTL (42-day fitness), ATL (7-day fatigue), TSB (form)
• Race Predictions: VDOT (Jack Daniels), Riegel formula
• Sleep & Recovery: NSF/AASM scoring, HRV-based recovery, TSB normalization
• Nutrition: Mifflin-St Jeor BMR, TDEE, macros, USDA FoodData Central (350k+ foods)
• Pattern Detection: Training consistency, hard/easy alternation, volume progression
• Configurable Algorithms: Runtime selection via environment variables

See Intelligence Methodology and Nutrition Methodology.

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
• 45 MCP Tools: Activities, goals, analysis, sleep, recovery, nutrition, configuration
• TypeScript SDK: pierre-mcp-client npm package
• Pluggable Providers: Compile-time provider selection

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
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.

LLM Interaction

AI assistants query fitness data through natural language:

Request	Tools Used
"Calculate my daily nutrition needs for marathon training"	calculate_daily_nutrition, calculate_nutrient_timing, search_foods
"Get my last 10 activities and analyze training load"	get_activities, analyze_training_load, calculate_daily_nutrition
"Compare my three longest runs this month"	get_activities, compare_activities, analyze_performance_trends
"Analyze this meal: 150g chicken, 200g rice, 100g broccoli"	analyze_meal_nutrition, get_food_details
"Do I need a recovery day based on my training load?"	analyze_training_load, get_activities, generate_recommendations

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/start-server.sh

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

45 tools organized in 8 categories:

Category	Tools	Description
Core Fitness	9	Activities, athlete profile, provider connections
Goals	4	Goal setting, suggestions, feasibility, progress
Analysis	8	Metrics, trends, patterns, recommendations
Sleep & Recovery	5	Sleep quality, recovery score, rest recommendations
Nutrition	5	BMR/TDEE, macros, USDA food search, meal analysis
Configuration	7	User settings, training zones, profiles
Fitness Config	4	Fitness parameters, thresholds
OAuth	5	Notifications, connection management

Full tool reference: src/protocols/universal/tool_registry.rs

Server Management

./bin/start-server.sh     # start backend (loads .envrc)
./bin/stop-server.sh      # stop backend
./bin/start-frontend.sh   # start dashboard (http://localhost:5173)

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.

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
• Contributing - code standards, PR workflow

Learning

• Tutorial (25 chapters) - comprehensive Rust learning path

Components

• SDK - TypeScript client for MCP integration
• Frontend - React dashboard

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.