Use It To
🗣️ Natural Language Commands
Turn natural-language instructions into ROS 2 actions (pub/sub, service calls). Simply tell your AI assistant what you want your robot to do, and MCP ROS2 handles the translation.
GitHub🔌 Universal AI Integration
Wire editors and shells to robots via the open MCP standard ("USB-C for AI apps"). Connect Claude Desktop, Copilot-style tools, or any MCP-compatible client to your robotics infrastructure.
Anthropic MCP⚡ Advanced Workflows (Pro)
Run multi-step flows and parallel streams with our Pro version. Execute complex robotic procedures with higher-throughput workflows and advanced orchestration capabilities.
GitHubWhy It Matters
Future-Proof AI Integration
MCP is becoming a cross-vendor way to connect agents to real tools—now including robotics platforms—so choosing MCP ROS2 keeps your robotics interface aligned with where the AI ecosystem is going.
The VergeCore Features
- Open-source core with community support
- Topic discovery and real-time subscription
- Service calls with parameter validation
- Historical data queries from Data Black Box
- Multi-topic publish/subscribe (Pro)
- Enterprise security and access controls (Pro)
Compatible Ecosystem
Works with leading AI platforms and development tools
Claude Desktop
Native integration with Anthropic's Claude
Development Tools
Terminal, VS Code, and editor integrations
Custom Clients
Build your own MCP-compatible tools
Intelligent Robotics Communication
MCP ROS2 revolutionizes how AI models interact with robotic systems by providing a standardized, high-performance bridge that enables real-time data exchange and intelligent decision-making across your robotics infrastructure.
🔗 Key Benefits
- Real-time AI model integration with ROS 2
- Standardized communication protocols
- Low-latency data exchange (sub-millisecond)
- Scalable across distributed robotics fleets
Available on MCP Market
Get MCP ROS2 Pro with advanced features and enterprise support.
Get MCP ROS2 ProHow MCP ROS2 Works
AI Model Integration
Connect any AI model to your ROS 2 ecosystem with standardized interfaces and automatic data serialization.
- TensorFlow, PyTorch, ONNX support
- Automatic tensor conversion
- Model versioning and hot-swapping
- GPU acceleration support
Real-time Processing
Ultra-low latency communication ensuring your robots can make intelligent decisions in real-time.
- Sub-millisecond latency
- Zero-copy data transfer
- Parallel processing pipelines
- Deterministic scheduling
Distributed Architecture
Scale across multiple robots and edge devices with intelligent load balancing and fault tolerance.
- Multi-robot coordination
- Edge computing optimization
- Automatic failover
- Dynamic load balancing
Technical Architecture
Protocol Stack
AI Model ↔ MCP Bridge ↔ ROS 2 Network
- Application Layer: AI model interfaces and APIs
- Protocol Layer: Model Context Protocol implementation
- Transport Layer: DDS/RTPS for ROS 2 communication
- Network Layer: UDP/TCP with quality of service
Data Flow
1. Sensor Data Collection
Robot sensors gather environmental data through ROS 2 topics
2. Real-time Processing
MCP ROS2 converts and routes data to appropriate AI models
3. Intelligent Decision
AI models process data and generate control commands
4. Robotic Action
Commands are executed through ROS 2 action servers
Integration Examples
Computer Vision Pipeline
camera/image_raw → MCP Bridge → YOLO Model → object_detection/results
Real-time object detection with automatic bounding box publishing to ROS 2 topics.
- Image preprocessing and normalization
- Model inference with GPU acceleration
- Result formatting for ROS 2 messages
- Visualization and debugging tools
Natural Language Commands
voice/audio → Speech-to-Text → NLP Model → robot/commands
Voice-controlled robotics with natural language understanding and intent recognition.
- Speech recognition and transcription
- Intent classification and entity extraction
- Command mapping to robot actions
- Multi-language support
Predictive Maintenance
diagnostics/sensors → Anomaly Detection → maintenance/alerts
AI-powered predictive maintenance with anomaly detection and failure prediction.
- Sensor data aggregation and analysis
- Machine learning-based anomaly detection
- Maintenance scheduling optimization
- Performance trend analysis
Path Planning
map/occupancy → A* + ML → navigation/path
Intelligent path planning with machine learning-enhanced algorithms and dynamic obstacle avoidance.
- Real-time map processing
- Dynamic obstacle detection
- Optimal path computation
- Multi-robot coordination
Performance Metrics
Start Building Intelligent Robots
Transform your robotics with AI-powered decision making through MCP ROS2
Free community version available • Enterprise support • Custom integrations