Race MCP Server

A comprehensive MCP (Model Context Protocol) server for iRacing telemetry data that provides real-time racing advice, car spotting, telemetry analysis, and conversational interaction about racing data.

🏁 Overview

This MCP server transforms iRacing telemetry data into actionable racing insights through:

• Real-time telemetry streaming with advanced analytics

• AI-powered racing coaching based on driving patterns

• Intelligent car spotting and traffic awareness

• Detailed lap analysis with improvement suggestions

• Session monitoring with performance trends

• Conversational interface for natural racing discussions

🚀 Features

Core Capabilities

• Live Telemetry Processing: Real-time data from iRacing via pyirsdk

• Advanced Analytics: G-force calculations, racing line analysis, gear optimization

• Racing AI Coach: Personalized advice based on driving style and situation

• Car Spotting System: Traffic awareness and positioning intelligence

• Performance Analysis: Lap-by-lap breakdown with improvement suggestions

• Session Tracking: Long-term performance trends and statistics

MCP Integration

• 5 Interactive Tools: Direct telemetry access and analysis functions

• 3 Live Resources: Streaming data feeds for continuous monitoring

• 3 AI Prompts: Specialized coaching personalities (coach, spotter, setup analyst)

• JSON-RPC 2.0: Standard MCP protocol compliance

• STDIO Transport: Ready for integration with MCP clients

📦 Installation

Prerequisites

• Python 3.8+

• iRacing simulator (for live telemetry)

• Virtual environment (recommended)

Quick Setup

# Clone the repository
git clone <your-repo-url>
cd race-mcp

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install the package with dependencies
pip install -e .

Manual Dependencies

If you prefer manual installation:

pip install mcp pyirsdk pydantic aiofiles

🎮 Usage

Running the Server

# Activate virtual environment
source .venv/bin/activate

# Run the MCP server directly
python -m race_mcp_server

# Or use the convenient startup script
./start_server.sh

# Run with debug logging
./start_server.sh --debug

# Run in simulation mode (no iRacing required)
./start_server.sh --simulation

Mock iRacing Data Generator

For development without the simulator, a standalone process can emit realistic iRacing-style telemetry over TCP.

# Start the mock telemetry stream on port 9000
python -m race_mcp_server.mock_iracing_stream --port 9000

Clients can connect to the specified host and port to receive newline-delimited JSON telemetry frames that mimic the structure of a real iRacing stream.

Launching with a GUI

After installation, you can start the server using a small GUI:

race-mcp-gui

The GUI offers start/stop controls and displays server logs for quick race prep.

Testing the Server

# Run comprehensive tests
python test_client.py

# Or use the startup script with testing
./start_server.sh --test

🎙 Voice Interaction

Real-time voice communication is supported through optional audio utilities.

Prerequisites

• OpenAI API key set in OPENAI_API_KEY

• Install voice dependencies: pip install race-mcp-server[voice]

Example Usage

import asyncio
from race_mcp_server.openai_client import OpenAIClient
from race_mcp_server.voice_interface import VoiceInterface

async def main():
    client = OpenAIClient()
    voice = VoiceInterface(client)
    await voice.chat_once()

asyncio.run(main())

This records a short microphone clip, sends it to OpenAI for transcription and speaks the model's response back to the user.

🛠 Available Tools

1. get_telemetry

Purpose: Retrieve current telemetry data with enhanced analytics

{
  "name": "get_telemetry",
  "arguments": {
    "include_analytics": true,
    "format": "detailed"
  }
}

Returns: Complete telemetry data including speed, RPM, position, G-forces, and racing line analysis

2. spot_cars

Purpose: Identify and analyze nearby cars for situational awareness

{
  "name": "spot_cars",
  "arguments": {
    "radius_meters": 100,
    "include_predictions": true
  }
}

Returns: List of nearby cars with relative positions, speeds, and trajectory predictions

3. get_racing_advice

Purpose: Get AI-powered coaching advice based on current situation

{
  "name": "get_racing_advice", 
  "arguments": {
    "context": "struggling with turn 3 entry speed",
    "focus_area": "cornering"
  }
}

Returns: Personalized advice with priority levels and specific recommendations

4. analyze_lap

Purpose: Detailed analysis of lap performance with improvement suggestions

{
  "name": "analyze_lap",
  "arguments": {
    "lap_number": 5,
    "compare_to_best": true
  }
}

Returns: Sector times, racing line efficiency, consistency ratings, and specific improvement areas

5. track_session

Purpose: Monitor overall session progress and performance trends

{
  "name": "track_session",
  "arguments": {
    "include_trends": true,
    "format": "summary"
  }
}

Returns: Session statistics, pace trends, fuel usage, and tire degradation analysis

📊 Live Resources

1. telemetry://live-stream

Continuous telemetry data stream with real-time updates

2. session://current-info

Current session information including track, conditions, and session type

3. track://layout-info

Track-specific information including turn locations and racing line data

🤖 AI Prompts

1. racing_coach

Personality: Professional racing instructor Focus: Technique improvement and strategic advice Use Case: General racing improvement and learning

2. car_spotter

Personality: Experienced spotter focused on safety and positioning Focus: Traffic management and situational awareness
Use Case: Race situations and traffic navigation

3. setup_analyst

Personality: Technical setup engineer Focus: Vehicle setup optimization and handling analysis Use Case: Car setup tuning and technical adjustments

🧪 Testing

Run the Test Client

The test client provides a comprehensive way to interact with all server functionality:

# Test all server functionality
python test_client.py

# Use the startup script for automated testing
./start_server.sh --test

Test Client Features

• Tool Testing: Tests all 5 MCP tools with realistic parameters

• Resource Access: Validates all 3 live resources

• Prompt Testing: Exercises all 3 AI coaching prompts

• Error Handling: Tests server resilience and error responses

• Performance: Shows response times and data validation

Manual Server Testing

# Start server manually and test with direct JSON-RPC calls
python -m race_mcp_server

# In another terminal, you can send JSON-RPC messages via stdin
# (Advanced usage - test_client.py is much easier)

🔧 Configuration

Environment Variables

# Enable debug logging
export MCP_DEBUG=1

# Force simulation mode
export RACE_MCP_SIMULATION=1

# Set custom iRacing data path (if needed)
export IRSDK_PATH=/path/to/irsdk

Server Configuration

Modify settings in src/race_mcp_server/main.py:

• Telemetry update intervals

• Simulation mode parameters

• Logging levels

• Analysis parameters

Startup Script Options

./start_server.sh --help    # Show all available options
./start_server.sh --check   # Verify system requirements

🔍 Troubleshooting

Common Issues

1. "pyirsdk connection failed"

   • Ensure iRacing is running and in a session

   • Check that iRacing telemetry output is enabled

   • The server will run in simulation mode if iRacing isn't available

2. "Tool execution failed"

   • Check server logs for detailed error messages

   • Verify the tool arguments match the expected schema

   • Try restarting the server

3. "Resource access failed"

   • Resources return simulated data when iRacing isn't running

   • Check that the server started without errors

   • Verify the resource URIs are correct

Debug Mode

# Enable verbose logging
python -m race_mcp_server --debug

# View detailed telemetry processing
export MCP_DEBUG=1 python -m race_mcp_server

Log Files

• Server logs: Written to console (redirect to file if needed)

• iRacing connection status: Logged at startup

• Tool execution: Logged for each request

🏗 Development

Project Structure

race-mcp/
├── src/race_mcp_server/
│   ├── __init__.py              # Package initialization
│   ├── __main__.py              # CLI entry point  
│   ├── main.py                  # Core MCP server (580+ lines)
│   └── telemetry_processor.py   # Advanced analytics engine
├── start_server.sh              # Convenient startup script
├── pyproject.toml               # Project configuration
├── test_client.py               # Comprehensive test suite
└── README.md                    # This file

Adding New Features

1. New Tools: Add methods to RaceMCPServer class in main.py

2. New Resources: Extend the resource handlers

3. Enhanced Analytics: Modify telemetry_processor.py

4. New Prompts: Add prompt templates to the prompts section

Testing New Features

# Test after making changes
python test_client.py

# Test with debug output
./start_server.sh --debug --test

Contributing

1. Fork the repository

2. Create a feature branch

3. Add tests for new functionality

4. Submit a pull request with detailed description

📋 Requirements

Core Dependencies

mcp = ">=1.0.0"          # Model Context Protocol framework
pyirsdk = ">=1.3.0"      # iRacing SDK integration
pydantic = ">=2.0.0"     # Data validation and serialization
aiofiles = ">=0.8.0"     # Async file operations

System Requirements

• OS: Windows, macOS, or Linux

• Python: 3.8+ (tested with 3.10.12)

• Memory: 100MB+ available

• iRacing: Any recent version with telemetry enabled (optional - server runs in simulation mode without it)

Optional Dependencies

• pytest: For running extended test suites

• MCP Client: Any MCP-compatible client for integration

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🏆 Acknowledgments

• iRacing: For providing the comprehensive telemetry API

• MCP Community: For the excellent Model Context Protocol framework

• Racing Community: For feedback and feature suggestions

Ready to improve your lap times? Start the server and run the test client to see it in action! 🏎️💨

Configuration

The server can be configured via environment variables:

• IRACING_TELEMETRY_INTERVAL - Telemetry update interval in seconds (default: 1.0)

• RACE_MCP_LOG_LEVEL - Logging level (default: INFO)

• RACE_MCP_ENABLE_SPOTTING - Enable car spotting features (default: true)

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   iRacing Sim   │───▶│  pyirsdk Stream │───▶│   MCP Server    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Claude/AI     │◀───│  MCP Protocol   │◀───│  Tool Handlers  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Development

# Run tests
pytest

# Format code
black src/
isort src/

# Type checking
mypy src/

License

MIT License