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Ultralytics MCP Server

by MetehanYasar11
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🚀 Ultralytics MCP Server - AI-Powered Computer Vision Platform

Unified Development Platform for YOLO Models with N8N Integration

A comprehensive Model Context Protocol (MCP) server that seamlessly integrates Ultralytics YOLO models with N8N workflows, providing a complete AI-powered computer vision solution in a single command.

✨ Features

🎯 Core Capabilities

  • 7 AI-Powered Tools for comprehensive YOLO operations
  • Real-time Object Detection with live inference
  • Model Training & Fine-tuning with custom datasets
  • Performance Analytics via TensorBoard integration
  • N8N Workflow Integration for automation

🖥️ User Interfaces

  • Streamlit Dashboard - Interactive web interface for model management
  • Jupyter Lab - Notebook environment for development
  • TensorBoard - Real-time training metrics and visualization
  • N8N Integration - Workflow automation and AI task orchestration

🔧 Technical Stack

  • CUDA 12.4.1 - GPU acceleration for training and inference
  • PyTorch - Deep learning framework with CUDA support
  • Ultralytics YOLO - State-of-the-art object detection models
  • Docker - Containerized deployment
  • Node.js MCP Server - Model Context Protocol implementation

🚀 Quick Start

Prerequisites

  • Docker Desktop with GPU support
  • NVIDIA drivers compatible with CUDA 12.4.1
  • Windows PowerShell or Linux/macOS terminal

One-Command Deployment

docker-compose up -d

That's it! The entire platform will be available at:

  • 🌐 Streamlit UI: http://localhost:8501
  • 📊 TensorBoard: http://localhost:6006
  • 📓 Jupyter Lab: http://localhost:8888
  • 🔗 MCP Server: http://localhost:8092

🎮 Available Services

ServicePortDescriptionStatus
Streamlit Dashboard8501Interactive YOLO model interface✅ Ready
MCP Server8092N8N integration endpoint✅ Ready
TensorBoard6006Training metrics visualization✅ Ready
Jupyter Lab8888Development environment✅ Ready

🛠️ MCP Tools Available

Our MCP server provides 7 specialized tools for AI workflows:

  1. detect_objects - Real-time object detection in images
  2. train_model - Custom YOLO model training
  3. evaluate_model - Model performance assessment
  4. predict_batch - Batch processing for multiple images
  5. export_model - Model format conversion (ONNX, TensorRT, etc.)
  6. benchmark_model - Performance benchmarking
  7. analyze_dataset - Dataset statistics and validation

🔌 N8N Integration

Connect to N8N using our MCP server:

  1. Server Endpoint: http://localhost:8092
  2. Transport: Server-Sent Events (SSE)
  3. Health Check: http://localhost:8092/health

Example N8N Workflow

{ "mcp_connection": { "transport": "sse", "endpoint": "http://localhost:8092/sse" } }

📁 Project Structure

ultralytics_mcp_server/ ├── 🐳 docker-compose.yml # Orchestration configuration ├── 🔧 Dockerfile.ultralytics # CUDA-enabled Ultralytics container ├── 🔧 Dockerfile.mcp-connector # Node.js MCP server container ├── 📦 src/ │ └── server.js # MCP server implementation ├── 🎨 main_dashboard.py # Streamlit main interface ├── 📄 pages/ # Streamlit multi-page app │ ├── train.py # Model training interface │ └── inference.py # Inference interface ├── ⚡ startup.sh # Container initialization script ├── 📋 .dockerignore # Build optimization └── 📖 README.md # This documentation

🔧 Configuration

Environment Variables

  • CUDA_VISIBLE_DEVICES - GPU device selection
  • STREAMLIT_PORT - Streamlit service port (default: 8501)
  • MCP_PORT - MCP server port (default: 8092)
  • TENSORBOARD_PORT - TensorBoard port (default: 6006)

Custom Configuration

Edit docker-compose.yml to customize:

  • Port mappings
  • Volume mounts
  • Environment variables
  • Resource limits

📊 Usage Examples

Object Detection via Streamlit

  1. Navigate to http://localhost:8501
  2. Upload an image or video
  3. Select YOLO model (YOLOv8, YOLOv11)
  4. Run inference and view results

Training Custom Models

  1. Access Jupyter Lab at http://localhost:8888
  2. Prepare your dataset in YOLO format
  3. Use the training interface in Streamlit
  4. Monitor progress in TensorBoard

N8N Automation

  1. Create N8N workflow
  2. Add MCP connector node
  3. Configure endpoint: http://localhost:8092
  4. Use available tools for automation

🔍 Monitoring & Debugging

Container Status

docker ps docker-compose logs ultralytics-container docker-compose logs mcp-connector-container

Health Checks

# MCP Server curl http://localhost:8092/health # Streamlit curl http://localhost:8501/_stcore/health # TensorBoard curl http://localhost:6006

🔄 Restart & Maintenance

Restart Services

docker-compose restart

Update & Rebuild

docker-compose down docker-compose up --build -d

Clean Reset

docker-compose down docker system prune -f docker-compose up --build -d

🎯 Performance Optimization

  • GPU Memory: Automatically managed by CUDA runtime
  • Batch Processing: Optimized for multiple image inference
  • Model Caching: Pre-loaded models for faster response
  • Multi-threading: Concurrent request handling

🚨 Troubleshooting

Common Issues

Container Restart Loop

# Check logs docker-compose logs ultralytics-container # Restart with rebuild docker-compose down docker-compose up --build -d

Streamlit Not Loading

# Verify container status docker ps # Check if files are copied correctly docker exec ultralytics-container ls -la /ultralytics/

GPU Not Detected

# Check NVIDIA drivers nvidia-smi # Verify CUDA in container docker exec ultralytics-container nvidia-smi

🔧 Development

Local Development Setup

  1. Clone repository
  2. Install dependencies: npm install (for MCP server)
  3. Set up Python environment for Streamlit
  4. Run services individually for debugging

Adding New MCP Tools

  1. Edit src/server.js
  2. Add tool definition in tools array
  3. Implement handler in handleToolCall
  4. Test with N8N integration

🤝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open Pull Request

📄 License

This project is licensed under the AGPL-3.0 License - see the Ultralytics License for details.

🙏 Acknowledgments

  • Ultralytics - For the amazing YOLO implementation
  • N8N - For the workflow automation platform
  • Streamlit - For the beautiful web interface framework
  • NVIDIA - For CUDA support and GPU acceleration

📞 Support

Made with ❤️ for the AI Community

🚀 Ready to revolutionize your computer vision workflows? Start with docker-compose up -d!

This server cannot be installed

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security - not tested
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quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A Model Context Protocol compliant server that provides RESTful API access to Ultralytics YOLO operations for computer vision tasks including training, validation, prediction, export, tracking, and benchmarking.

  1. ✨ Features
    1. 🎯 Core Capabilities
    2. 🖥️ User Interfaces
    3. 🔧 Technical Stack
  2. 🚀 Quick Start
    1. Prerequisites
    2. One-Command Deployment
  3. 🎮 Available Services
    1. 🛠️ MCP Tools Available
      1. 🔌 N8N Integration
        1. Example N8N Workflow
      2. 📁 Project Structure
        1. 🔧 Configuration
          1. Environment Variables
          2. Custom Configuration
        2. 📊 Usage Examples
          1. Object Detection via Streamlit
          2. Training Custom Models
          3. N8N Automation
        3. 🔍 Monitoring & Debugging
          1. Container Status
          2. Health Checks
        4. 🔄 Restart & Maintenance
          1. Restart Services
          2. Update & Rebuild
          3. Clean Reset
        5. 🎯 Performance Optimization
          1. 🚨 Troubleshooting
            1. Common Issues
          2. 🔧 Development
            1. Local Development Setup
            2. Adding New MCP Tools
          3. 🤝 Contributing
            1. 📄 License
              1. 🙏 Acknowledgments
                1. 📞 Support

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