Autoware MCP Server
Universal bridge between AI agents and Autoware autonomous driving stack, enabling AI-driven mission planning, real-time vehicle control, and adaptive decision-making.
README
Autoware MCP Integration
The Autoware MCP Server provides a universal bridge between ANY AI agent (Claude, GPT, Gemini, or custom agents) and the Autoware autonomous driving stack through the Model Context Protocol (MCP). This enables AI-driven mission planning, real-time vehicle control, and adaptive decision-making for autonomous vehicles.
Click the image above to watch the full autonomous driving demo
Key Features
- Universal AI Support: Works with any MCP-compatible AI agent (Claude, GPT, Gemini, etc.)
- Full Vehicle Control: Complete control over Autoware's autonomous driving features
- Real-Time Monitoring: Concurrent monitoring of perception, planning, and vehicle state
- Launch Management: Start, stop, and manage ROS2 launch sessions
- Safety Systems: Comprehensive safety validation at every level
- Mission Execution: Support for complex multi-step missions with waypoints
Quick Start
Prerequisites
- Ubuntu 22.04 LTS
- ROS2 Humble
- Python 3.10+
- Autoware 0.45.1
Installation
# Clone the repository
git clone https://github.com/your-org/autoware-mcp.git
cd autoware-mcp
# Install uv (Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install dependencies with uv
uv sync --all-extras --dev
Basic Usage
- Start the MCP Server:
# Source your ROS2/Autoware workspace
source ~/autoware/install/setup.bash
# Start the MCP server
uv run autoware-mcp
- Connect with Claude Code:
Add to your Claude Code configuration file (
claude_desktop_config.json):
{
"mcpServers": {
"autoware": {
"command": "uv",
"args": ["run", "autoware-mcp"],
"env": {
"ROS_DOMAIN_ID": "your_domain_id"
}
}
}
}
- Example Prompts for AI Agents:
Since AI agents may not have specific knowledge about autonomous driving, use clear, step-by-step prompts:
To launch a planning simulation:
Please launch a planning simulation for Autoware using the MCP tools.
Use the start_launch tool with the planning_simulation_mcp.launch.py file.
To run an autonomous driving sequence:
Please run the complete autonomous driving sequence:
1. Initialize the vehicle's localization at the starting position
2. Clear any existing route and set a new route to the goal position
3. Change the operation mode to autonomous
4. Monitor the vehicle's progress until it reaches the destination
5. Stop the simulation when complete
To monitor vehicle state:
Please check the current vehicle state including position, speed, and route status
using the get_vehicle_state and get_current_route MCP tools.
Available MCP Tools
System Management
health_check- Get complete system health statuscheck_autoware_status- Check running Autoware componentsverify_ros2_environment- Verify ROS2 setup
Launch Management
start_launch- Start ROS2 launch filesstop_launch- Stop launch sessionslist_launch_sessions- View active sessionsget_session_status- Get detailed session information
Vehicle Control
set_operation_mode- Change between stop/autonomous/local/remote modesinitialize_localization- Set initial vehicle poseset_route- Set route to destinationset_route_points- Set route with waypointsget_current_route- Get current route status
Vehicle Monitoring
get_vehicle_state- Get vehicle position, speed, and kinematicsmonitor_operation_mode- Monitor current operation modemonitor_localization_state- Check localization statusmonitor_motion_state- Monitor motion readiness
ROS2 Interface
list_ros2_nodes- List all active ROS2 nodeslist_ros2_topics- List all ROS2 topicslist_ros2_services- List all ROS2 servicescall_ros2_service- Call any ROS2 servicepublish_to_topic- Publish to any ROS2 topicecho_topic_messages- Capture messages from topics
Documentation
For comprehensive documentation, please refer to the Developer Guide
Configuration
Create a configuration file config/custom.yaml:
mcp_server:
host: 0.0.0.0
port: 8080
autoware:
ros_domain_id: 42
ad_api_url: http://localhost:8888
safety:
max_velocity: 50.0 # m/s
max_acceleration: 3.0 # m/s²
emergency_deceleration: -8.0 # m/s²
Development
Running Tests
# Unit tests
pytest tests/unit -v
# Integration tests (requires Autoware)
pytest tests/integration -v
# Simulation tests
pytest tests/simulation -v
# All tests with coverage
pytest --cov=autoware_mcp --cov-report=html
Contributing
Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.
Safety Notice
WARNING: This system is designed for research and development purposes. Always ensure:
- A safety driver is present during real vehicle testing
- Emergency stop mechanisms are properly configured
- All safety validations are enabled
- Testing is conducted in controlled environments
Troubleshooting
Common Issues
- ROS2 Connection Issues:
export ROS_DOMAIN_ID=42
export RMW_IMPLEMENTATION=rmw_cyclonedds_cpp
- MCP Server Not Responding:
- Check firewall settings
- Verify Autoware is running
- Check logs:
tail -f logs/mcp_server.log
- Performance Issues:
- Monitor resource usage:
htop,nvidia-smi - Check ROS2 topics:
ros2 topic hz /topic_name - Review latency metrics in Grafana dashboard
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Acknowledgments
- Autoware Foundation for the autonomous driving stack
- Model Context Protocol contributors
- ROS2 and Open Robotics community
Support
For issues and questions:
- GitHub Issues: Create an issue
- Documentation: Read the docs
- Community: Join our Discord
Roadmap
- [ ] Multi-vehicle coordination support
- [ ] Enhanced perception monitoring
- [ ] Cloud-based mission management
- [ ] V2X communication integration
- [ ] Advanced ML-based planning
- [ ] Simulation environment integration
Note: This project is under active development. APIs and features may change.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
