Pierre Fitness Platform

Development Status: This project is under active development. APIs and features may change.

Pierre Fitness Platform connects AI assistants to fitness data from Strava, Garmin, and Fitbit. The platform implements the Model Context Protocol (MCP), A2A protocol, OAuth 2.0, and REST APIs for integration with Claude, ChatGPT, and other AI assistants.

Intelligence System

The platform calculates fitness metrics using established sports science formulas:

Training Load: TSS (Training Stress Score) from power or heart rate data, CTL (42-day fitness), ATL (7-day fatigue), TSB (form indicator)
Race Predictions: VDOT-based predictions using Jack Daniels' VO2max formula, Riegel formula for distance scaling
Statistical Analysis: Linear regression for performance trends, R² coefficient for fit quality, moving averages for smoothing
Pattern Detection: Weekly training schedule consistency, hard/easy workout alternation, volume progression analysis
Sleep and Recovery: NSF/AASM-based sleep quality scoring, TSB normalization, HRV-based recovery assessment, weighted recovery calculations
Nutrition and USDA Integration: Mifflin-St Jeor BMR, TDEE with activity factors, sport-specific protein/carb/fat recommendations, nutrient timing, USDA FoodData Central integration (350,000+ foods)
Physiological Validation: Bounds checking for heart rate (100-220 bpm max), power (50-600W FTP), VO2 max (20-90 ml/kg/min)
Configurable Algorithms: All calculation algorithms support multiple variants (e.g., Daniels vs Riegel for VDOT, Bannister vs Edwards for TRIMP) selectable via environment variables for runtime customization

See Intelligence and Analytics Methodology and Nutrition Methodology for formulas, implementation details, and scientific references. Sleep and recovery analysis is documented in the Intelligence Methodology. See Algorithm Configuration for available algorithm variants and configuration options.

Features

MCP Protocol: JSON-RPC over HTTP for AI assistant integration
OAuth 2.0 Server: RFC 7591 dynamic client registration for MCP clients
RS256/JWKS: Asymmetric JWT signing with public key distribution
A2A Protocol: Agent-to-agent communication with capability discovery
Multi-Tenancy: Isolated data and configuration per organization
Real-Time Updates: Server-Sent Events for OAuth notifications
Plugin System: Compile-time plugin architecture with lifecycle management
PII Redaction: Middleware for sensitive data removal in logs and responses
Cursor Pagination: Keyset pagination for consistent large dataset traversal
Intelligent Caching: LRU cache with TTL for API response optimization
Atomic Operations: TOCTOU prevention with database-level atomic token operations
Structured Error Handling: Type-safe error propagation with AppError/DatabaseError/ProviderError

Architecture

Pierre Fitness Platform runs as a single HTTP server on port 8081 (configurable). All protocols (MCP, OAuth 2.0, REST API) share the same port.

mcp transport modes

┌──────────────────────────────────────────────────────────────────────────────────────┐
│                              MCP Client Integration                                  │
├──────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                      │
│  stdio transport (subprocess-based)                                                  │
│  ┌─────────────────┐    stdio     ┌─────────────────┐    HTTP+OAuth   ┌──────────┐   │
│  │   MCP Client    │ ◄─────────►  │ Pierre SDK      │ ◄─────────────► │ Pierre   │   │
│  │ (Claude Desktop)│              │ Bridge          │                 │ Fitness  │   │
│  └─────────────────┘              └─────────────────┘                 │ Platform │   │
│                                                                       │          │   │
│  streamable http transport (server-based)                             │          │   │
│  ┌─────────────────┐    MCP-over-HTTP+OAuth                           │          │   │
│  │   MCP Client    │ ◄──────────────────────────────────────────────► │          │   │
│  │ (HTTP-native)   │                                                  │          │   │
│  └─────────────────┘                                                  └──────────┘   │
│                                                                                      │
└──────────────────────────────────────────────────────────────────────────────────────┘

stdio transport (via pierre-mcp-client npm package)

mcp clients spawn server as subprocess and communicate via stdin/stdout
for mcp clients using stdio transport (claude desktop, chatgpt, most existing clients)
sdk bridge handles oauth 2.0 flow and token management automatically
configuration: add sdk command to mcp client config (see mcp client integration section)

streamable http transport (direct http connection)

mcp clients connect directly to pierre's http endpoint
for mcp clients with streamable http transport support
direct mcp-over-http communication with oauth 2.0 authentication
configuration: implement oauth 2.0 flow and connect to http://localhost:8081/mcp

LLM Interaction

AI assistants query fitness data through natural language. The LLM determines which MCP tools to call and combines results.

Example Interactions

Natural Language Request	What Happens	Tools Used
"Calculate my daily nutrition needs for marathon training and suggest pre-workout meals"	Calculates BMR/TDEE based on user profile, determines macros for endurance goal, calculates nutrient timing, searches USDA database for suitable pre-workout foods	`calculate_daily_nutrition`, `calculate_nutrient_timing`, `search_foods`
"Get my last 10 activities and propose a week-long meal plan with protein targets based on my training load"	Retrieves recent activities, analyzes intensity and duration, calculates caloric expenditure, generates nutrition recommendations with macro breakdowns	`get_activities`, `analyze_training_load`, `calculate_daily_nutrition`
"Compare my three longest runs this month and identify areas for improvement"	Fetches top runs by distance, analyzes pace consistency, heart rate zones, elevation patterns, provides feedback	`get_activities`, `compare_activities`, `analyze_performance_trends`
"Analyze this meal: 150g chicken breast, 200g rice, 100g broccoli"	Looks up each food in USDA database, retrieves complete nutrient breakdown, calculates total macros and calories for the meal	`analyze_meal_nutrition`, `get_food_details`
"Check my training load for the last two weeks and tell me if I need a recovery day"	Calculates cumulative training stress, analyzes recovery metrics, provides rest recommendations	`analyze_training_load`, `get_activities`, `generate_recommendations`
"When's the best day this week for an outdoor run based on my typical schedule and weather conditions?"	Analyzes activity patterns, checks weather forecasts, recommends timing	`detect_patterns`, `get_activities`, weather integration

Quick Start

Prerequisites

Rust 1.70+
SQLite (default) or PostgreSQL (production)

Installation

git clone https://github.com/Async-IO/pierre_mcp_server.git
cd pierre_mcp_server
cargo build --release

Configuration

Using `.envrc` (Recommended)

Pierre Fitness Platform includes a .envrc file for environment configuration. Use direnv to automatically load environment variables:

# Install direnv (macOS)
brew install direnv

# Add to your shell profile (~/.zshrc or ~/.bashrc)
eval "$(direnv hook zsh)"  # or bash

# Allow direnv for this directory
cd pierre_mcp_server
direnv allow

The .envrc file includes all required configuration with development defaults. Edit .envrc to customize settings for your environment.

Manual Configuration

Required Environment Variables:

export DATABASE_URL="sqlite:./data/pierre.db"
export PIERRE_MASTER_ENCRYPTION_KEY="$(openssl rand -base64 32)"

Optional Environment Variables:

export HTTP_PORT=8081              # Server port (default: 8081)
export RUST_LOG=info               # Log level
export JWT_EXPIRY_HOURS=24         # JWT token expiry
export PIERRE_RSA_KEY_SIZE=4096    # RSA key size for JWT signing (default: 4096)

# Fitness provider OAuth (for data integration)
export STRAVA_CLIENT_ID=your_client_id
export STRAVA_CLIENT_SECRET=your_client_secret
export STRAVA_REDIRECT_URI=http://localhost:8081/api/oauth/callback/strava  # local dev only

# Garmin Connect OAuth (optional)
export GARMIN_CLIENT_ID=your_consumer_key
export GARMIN_CLIENT_SECRET=your_consumer_secret
export GARMIN_REDIRECT_URI=http://localhost:8081/api/oauth/callback/garmin  # local dev only

# Production: Use HTTPS for callback URLs
# export STRAVA_REDIRECT_URI=https://api.example.com/api/oauth/callback/strava
# export GARMIN_REDIRECT_URI=https://api.example.com/api/oauth/callback/garmin

# Weather data (optional)
export OPENWEATHER_API_KEY=your_api_key

# Algorithm configuration (optional - defaults optimized for most users)
export PIERRE_MAXHR_ALGORITHM=tanaka           # Max heart rate: fox, tanaka, nes, gulati
export PIERRE_TRIMP_ALGORITHM=hybrid           # Training impulse: bannister_male, bannister_female, edwards_simplified, lucia_banded, hybrid
export PIERRE_TSS_ALGORITHM=avg_power          # Training stress score: avg_power, normalized_power, hybrid
export PIERRE_VDOT_ALGORITHM=daniels           # Running performance: daniels, riegel, hybrid
export PIERRE_TRAINING_LOAD_ALGORITHM=ema      # Training load: ema, sma, wma, kalman
export PIERRE_RECOVERY_ALGORITHM=weighted      # Recovery aggregation: weighted, additive, multiplicative, minmax, neural
export PIERRE_FTP_ALGORITHM=from_vo2max        # Functional threshold power: 20min_test, 8min_test, ramp_test, from_vo2max, hybrid
export PIERRE_LTHR_ALGORITHM=from_maxhr        # Lactate threshold HR: from_maxhr, from_30min, from_race, lab_test, hybrid
export PIERRE_VO2MAX_ALGORITHM=from_vdot       # VO2max estimation: from_vdot, cooper, rockport, astrand, bruce, hybrid
# See docs/configuration.md#algorithm-configuration for details

# Cache configuration
export CACHE_MAX_ENTRIES=10000                    # Maximum cached entries (default: 10,000)
export CACHE_CLEANUP_INTERVAL_SECS=300            # Cleanup interval in seconds (default: 300)
export REDIS_URL=redis://localhost:6379           # Redis cache (optional, uses in-memory if not set)

See src/constants/mod.rs for all environment variables and default values.

Starting the Server

cargo run --bin pierre-mcp-server

The server will start on port 8081 and display available endpoints.

Initial Setup

Create an admin user via REST API:

curl -X POST http://localhost:8081/admin/setup \
  -H "Content-Type: application/json" \
  -d '{
    "email": "[email protected]",
    "password": "SecurePass123!",
    "display_name": "Admin"
  }'

MCP Client Integration

Pierre Fitness Platform includes an SDK bridge for direct integration with MCP clients that only support stdin/out. The SDK handles OAuth 2.0 authentication automatically.

SDK Installation

Option 1: Install from npm (Recommended)

npm install pierre-mcp-client@next

The SDK is published as a pre-release package (@next tag) during v0.x development.

Option 2: Build from source

The SDK is included in the sdk/ directory:

cd sdk
npm install
npm run build

MCP Client Configuration

Add Pierre to your MCP client configuration. For Claude Desktop:

Configuration File Location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json

Configuration (using npm package):

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

Alternative (using local installation):

{
  "mcpServers": {
    "pierre-fitness": {
      "command": "node",
      "args": [
        "/absolute/path/to/pierre_mcp_server/sdk/dist/cli.js",
        "--server",
        "http://localhost:8081"
      ]
    }
  }
}

Replace /absolute/path/to/ with your actual path for local installations.

Authentication Flow

When the MCP client starts, the SDK will:

Register an OAuth 2.0 client with Pierre Fitness Platform (RFC 7591)
Open your browser for authentication
Handle the OAuth callback and token exchange
Use JWT tokens for all MCP requests

No manual token management required.

Streamable http transport

Mcp clients with streamable http transport support can connect directly to pierre without the sdk bridge.

Http transport setup

Mcp clients with streamable http transport connect directly to the mcp endpoint:

endpoint: http://localhost:8081/mcp (development)
endpoint: https://your-server.com/mcp (production)

Http transport authentication

Streamable http connections use oauth 2.0 authorization code flow:

Client discovers oauth configuration from /.well-known/oauth-authorization-server
Client registers dynamically using rfc 7591 (/oauth2/register)
Opens browser for user authentication (/oauth2/authorize)
Exchanges authorization code for jwt token (/oauth2/token)
Uses jwt token for all subsequent mcp requests

Available MCP Tools

Pierre Fitness Platform provides 36 tools through the MCP protocol. Tool definitions are in src/protocols/universal/tool_registry.rs (ToolId enum).

Core Fitness Data

Tool	Description	Parameters
`get_activities`	Get user activities from fitness providers	`provider` (optional), `limit` (optional)
`get_athlete`	Get athlete profile information	None
`get_stats`	Get athlete statistics and metrics	None
`analyze_activity`	Analyze a specific activity with detailed insights	`activity_id` (required)
`get_activity_intelligence`	Get AI-powered analysis for an activity	`activity_id` (required)
`get_connection_status`	Check OAuth connection status for providers	None
`disconnect_provider`	Disconnect from a fitness provider	`provider` (required)

Goals and Progress

Tool	Description	Parameters
`set_goal`	Set a new fitness goal	`goal_type`, `target_value` (required)
`suggest_goals`	Get AI-suggested goals based on activity history	None
`analyze_goal_feasibility`	Analyze if a goal is achievable	`goal_data` (required)
`track_progress`	Track progress toward goals	`goal_id` (required)

Performance Analysis

Tool	Description	Parameters
`calculate_metrics`	Calculate custom fitness metrics	`activity_id` (required)
`analyze_performance_trends`	Analyze performance trends over time	None
`compare_activities`	Compare activities for performance analysis	`activity_ids` (required)
`detect_patterns`	Detect patterns in activity data	None
`generate_recommendations`	Generate personalized training recommendations	None
`calculate_fitness_score`	Calculate overall fitness score	None
`predict_performance`	Predict future performance based on training	None
`analyze_training_load`	Analyze training load and recovery	None

Sleep and Recovery

Tool	Description	Parameters
`analyze_sleep_quality`	Analyze sleep quality with NSF/AASM scoring	`sleep_session` (required)
`calculate_recovery_score`	Calculate recovery readiness from TSB, sleep, HRV	`tsb`, `sleep_quality`, `hrv_data` (optional)
`track_sleep_trends`	Track sleep patterns and trends over time	`start_date`, `end_date` (required)
`optimize_sleep_schedule`	Get personalized sleep timing recommendations	`preferences` (optional)
`suggest_rest_day`	Get rest day recommendations based on recovery	`tsb`, `recent_load`, `sleep_quality` (optional)

Configuration Management

Tool	Description	Parameters
`get_configuration_catalog`	Get complete configuration catalog	None
`get_configuration_profiles`	Get available configuration profiles	None
`get_user_configuration`	Get current user configuration	None
`update_user_configuration`	Update user configuration parameters	`profile` or `parameters` (required)
`calculate_personalized_zones`	Calculate personalized training zones	None
`validate_configuration`	Validate configuration parameters	`parameters` (required)

Tool descriptions from src/protocols/universal/tool_registry.rs (ToolId enum description method).

A2A (Agent-to-Agent) Protocol

Pierre Fitness Platform supports agent-to-agent communication for autonomous AI systems. Implementation in src/a2a/.

A2A Features:

Agent Cards for capability discovery (src/a2a/agent_card.rs)
Cryptographic authentication between agents
Asynchronous messaging protocol
Protocol versioning (A2A 1.0.0)

A2A Endpoints:

GET /a2a/status - Get A2A protocol status
GET /a2a/tools - Get available A2A tools
POST /a2a/execute - Execute A2A tool
GET /a2a/monitoring - Get A2A monitoring information
GET /a2a/client/tools - Get client-specific A2A tools
POST /a2a/client/execute - Execute client A2A tool

Example A2A Integration:

use pierre_mcp_server::a2a::A2AClientManager;

#[tokio::main]
async fn main() -> Result<()> {
    let client = A2AClientManager::new("https://pierre-server.com/a2a").await?;

    let response = client.send_message(
        "fitness-analyzer-agent",
        serde_json::json!({
            "action": "analyze_performance",
            "user_id": "user-123",
            "timeframe": "last_30_days"
        })
    ).await?;

    println!("Analysis: {}", response);
    Ok(())
}

Testing

Pierre Fitness Platform includes comprehensive test coverage with automated intelligence testing using synthetic data.

# Run all tests
cargo test

# Run specific test suites
cargo test --test mcp_protocol_comprehensive_test
cargo test --test mcp_multitenant_complete_test
cargo test --test intelligence_tools_basic_test
cargo test --test intelligence_tools_advanced_test

# Run with output
cargo test -- --nocapture

# Lint and test (comprehensive validation)
./scripts/lint-and-test.sh

Intelligence Testing Framework

The platform includes 30+ integration tests covering all 8 intelligence tools without OAuth dependencies:

Test Categories:

Basic Tools: get_athlete, get_activities, get_stats, compare_activities
Advanced Analytics: calculate_fitness_score, predict_performance, analyze_training_load
Goal Management: suggest_goals, analyze_goal_feasibility, track_progress

Synthetic Data Scenarios:

Beginner runner improving over time
Experienced cyclist with consistent training
Multi-sport athlete (triathlete pattern)
Training gaps and recovery periods

See tests/intelligence_tools_basic_test.rs and tests/intelligence_tools_advanced_test.rs for details.

RSA Key Size Configuration

Pierre Fitness Platform uses RS256 asymmetric signing for JWT tokens. Key size affects both security and performance:

Production (4096-bit keys - default):

Higher security with larger key size
Slower key generation (~10 seconds)
Use in production environments

Testing (2048-bit keys):

Faster key generation (~250ms)
Suitable for development and testing
Set via environment variable:

export PIERRE_RSA_KEY_SIZE=2048

Test Performance Optimization

Pierre Fitness Platform includes a shared test JWKS manager to eliminate RSA key generation overhead:

Shared Test JWKS Pattern (implemented in tests/common.rs:40-52):

use pierre_mcp_server_integrations::common;

// Reuses shared JWKS manager across all tests (10x faster)
let jwks_manager = common::get_shared_test_jwks();

Performance Impact:

Without optimization: 100ms+ RSA key generation per test
With shared JWKS: One-time generation, instant reuse across test suite
Result: 10x faster test execution

E2E Tests: The SDK test suite (sdk/test/) automatically uses 2048-bit keys via PIERRE_RSA_KEY_SIZE=2048 in server startup configuration (sdk/test/helpers/server.js:82).

Development Tools

Automated Setup

# Clean database and fresh server start
./scripts/fresh-start.sh
cargo run --bin pierre-mcp-server &

# Complete workflow test (admin + user + tenant + login + MCP)
./scripts/complete-user-workflow.sh

# Load saved environment variables
source .workflow_test_env
echo "JWT Token: ${JWT_TOKEN:0:50}..."

Management Dashboard

A web dashboard is available for monitoring:

cd frontend
npm install && npm run dev

Access at http://localhost:5173 for:

User management and approval
API key monitoring
Usage analytics
Real-time request monitoring

See frontend/README.md for details.

Documentation

Complete documentation is in the docs/ directory:

Getting Started - installation and quick start
Architecture - system design and components
Protocols - mcp, oauth2, a2a, rest protocols
Authentication - jwt, api keys, oauth2
OAuth Client - fitness provider oauth connections (strava, fitbit, garmin)
OAuth2 Server - mcp client authentication
Configuration - environment variables and settings
Contributing - development guidelines

Installation guide for MCP clients:

MCP Client Installation - claude desktop, chatgpt, and other mcp clients

Code Quality

Pierre Fitness Platform uses validation scripts to maintain code quality and prevent common issues:

Pre-commit Validation:

Pattern validation via scripts/validation-patterns.toml
Clippy linting with strict warnings
Test execution
Format checking

Run validation:

./scripts/lint-and-test.sh

Install git hooks:

./scripts/install-hooks.sh

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/new-feature)
Run validation (./scripts/lint-and-test.sh)
Commit changes (git commit -m 'feat: add new feature')
Push to branch (git push origin feature/new-feature)
Open a Pull Request

License

This project is dual-licensed:

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

You may choose either license.

Name		Name	Last commit message	Last commit date
Latest commit History 628 Commits
.githooks		.githooks
.github		.github
docker/postgres-init		docker/postgres-init
docs		docs
examples		examples
frontend		frontend
scripts		scripts
sdk		sdk
src		src
templates		templates
test_data		test_data
tests		tests
.dockerignore		.dockerignore
.env.postgres.example		.env.postgres.example
.envrc.example		.envrc.example
.gitignore		.gitignore
.mcp.json		.mcp.json
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
Dockerfile.test		Dockerfile.test
LICENSE		LICENSE
LICENSE-APACHE		LICENSE-APACHE
LICENSE-MIT		LICENSE-MIT
README.md		README.md
deny.toml		deny.toml
docker-compose-with-envrc.sh		docker-compose-with-envrc.sh
docker-compose.postgres.yml		docker-compose.postgres.yml
docker-compose.prod.yml		docker-compose.prod.yml
docker-compose.yml		docker-compose.yml
docker-entrypoint.sh		docker-entrypoint.sh
llms.txt		llms.txt
package.json		package.json
rust-toolchain		rust-toolchain

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Pierre Fitness Platform

Intelligence System

Features

Architecture

mcp transport modes

LLM Interaction

Example Interactions

Quick Start

Prerequisites

Installation

Configuration

Using .envrc (Recommended)

Manual Configuration

Starting the Server

Initial Setup

MCP Client Integration

SDK Installation

MCP Client Configuration

Authentication Flow

Streamable http transport

Http transport setup

Http transport authentication

Available MCP Tools

Core Fitness Data

Goals and Progress

Performance Analysis

Sleep and Recovery

Configuration Management

A2A (Agent-to-Agent) Protocol

Testing

Intelligence Testing Framework

RSA Key Size Configuration

Test Performance Optimization

Development Tools

Automated Setup

Management Dashboard

Documentation

Code Quality

Contributing

License

About

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Using `.envrc` (Recommended)