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https://github.com/GongRzhe/YOLO-MCP-Server


https://github.com/GongRzhe/YOLO-MCP-Server

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README 

          
# YOLO MCP Service



A powerful YOLO (You Only Look Once) computer vision service that integrates with Claude AI through Model Context Protocol (MCP). This service enables Claude to perform object detection, segmentation, classification, and real-time camera analysis using state-of-the-art YOLO models.



![](https://badge.mcpx.dev?type=server 'MCP Server')



## Features



- Object detection, segmentation, classification, and pose estimation

- Real-time camera integration for live object detection

- Support for model training, validation, and export

- Comprehensive image analysis combining multiple models

- Support for both file paths and base64-encoded images

- Seamless integration with Claude AI



## Setup Instructions



### Prerequisites



- Python 3.10 or higher

- Git (optional, for cloning the repository)



### Environment Setup



1. Create a directory for the project and navigate to it:

   ```bash

   mkdir yolo-mcp-service

   cd yolo-mcp-service

   ```



2. Download the project files or clone from repository:

   ```bash

   # If you have the files, copy them to this directory

   # If using git:

   git clone https://github.com/GongRzhe/YOLO-MCP-Server.git .

   ```



3. Create a virtual environment:

   ```bash

   # On Windows

   python -m venv .venv

   

   # On macOS/Linux

   python3 -m venv .venv

   ```



4. Activate the virtual environment:

   ```bash

   # On Windows

   .venv\Scripts\activate

   

   # On macOS/Linux

   source .venv/bin/activate

   ```



5. Run the setup script:

   ```bash

   python setup.py

   ```

   

   The setup script will:

   - Check your Python version

   - Create a virtual environment (if not already created)

   - Install required dependencies

   - Generate an MCP configuration file (mcp-config.json)

   - Output configuration information for different MCP clients including Claude



6. Note the output from the setup script, which will look similar to:

   ```

   MCP configuration has been written to: /path/to/mcp-config.json

   

   MCP configuration for Cursor:

   

   /path/to/.venv/bin/python /path/to/server.py

   

   MCP configuration for Windsurf/Claude Desktop:

   {

     "mcpServers": {

       "yolo-service": {

         "command": "/path/to/.venv/bin/python",

         "args": [

           "/path/to/server.py"

         ],

         "env": {

           "PYTHONPATH": "/path/to"

         }

       }

     }

   }

   

   To use with Claude Desktop, merge this configuration into: /path/to/claude_desktop_config.json

   ```



### Downloading YOLO Models



Before using the service, you need to download the YOLO models. The service looks for models in the following directories:

- The current directory where the service is running

- A `models` subdirectory

- Any other directory configured in the `CONFIG["model_dirs"]` variable in server.py



Create a models directory and download some common models:



```bash

# Create models directory

mkdir models



# Download YOLOv8n for basic object detection

curl -L https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt -o models/yolov8n.pt



# Download YOLOv8n-seg for segmentation

curl -L https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt -o models/yolov8n-seg.pt



# Download YOLOv8n-cls for classification

curl -L https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt -o models/yolov8n-cls.pt



# Download YOLOv8n-pose for pose estimation

curl -L https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt -o models/yolov8n-pose.pt

```



For Windows PowerShell users:

```powershell

# Create models directory

mkdir models



# Download models using Invoke-WebRequest

Invoke-WebRequest -Uri "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt" -OutFile "models/yolov8n.pt"

Invoke-WebRequest -Uri "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt" -OutFile "models/yolov8n-seg.pt"

Invoke-WebRequest -Uri "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt" -OutFile "models/yolov8n-cls.pt"

Invoke-WebRequest -Uri "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt" -OutFile "models/yolov8n-pose.pt"

```



### Configuring Claude



To use this service with Claude:



1. For Claude web: Set up the service on your local machine and use the configuration provided by the setup script in your MCP client.



2. For Claude Desktop:

   - Run the setup script and note the configuration output

   - Locate your Claude Desktop configuration file (the path is provided in the setup script output)

   - Add or merge the configuration into your Claude Desktop configuration file

   - Restart Claude Desktop



## Using YOLO Tools in Claude



### 1. First Check Available Models



Always check which models are available on your system first:



```

I'd like to use the YOLO tools. Can you first check which models are available on my system?



```



### 2. Detecting Objects in an Image



For analyzing an image file on your computer:



```

Can you analyze this image file for objects?



/path/to/your/image.jpg

0.3



```



You can also specify a different model:



```

Can you analyze this image using a different model?



/path/to/your/image.jpg

yolov8n.pt

0.4



```



### 3. Running Comprehensive Image Analysis



For more detailed analysis that combines object detection, classification, and more:



```

Can you perform a comprehensive analysis on this image?



/path/to/your/image.jpg

0.3



```



### 4. Image Segmentation



For identifying object boundaries and creating segmentation masks:



```

Can you perform image segmentation on this photo?



/path/to/your/image.jpg

true

yolov8n-seg.pt



```



### 5. Image Classification



For classifying the entire image content:



```

What does this image show? Can you classify it?



/path/to/your/image.jpg

true

yolov8n-cls.pt

5



```



### 6. Using Your Computer's Camera



Start real-time object detection using your computer's camera:



```

Can you turn on my camera and detect objects in real-time?



yolov8n.pt

0.3



```



Get the latest camera detections:



```

What are you seeing through my camera right now?



```



Stop the camera when finished:



```

Please turn off the camera.



```



### 7. Advanced Model Operations



#### Training a Custom Model



```

I want to train a custom object detection model on my dataset.



/path/to/your/dataset

yolov8n.pt

50



```



#### Validating a Model



```

Can you validate the performance of my model on a test dataset?



/path/to/your/trained/model.pt

/path/to/validation/dataset



```



#### Exporting a Model to Different Formats



```

I need to export my YOLO model to ONNX format.



/path/to/your/model.pt

onnx



```



### 8. Testing Connection



Check if the YOLO service is running correctly:



```

Is the YOLO service running correctly?



```



## Troubleshooting



### Camera Issues



If the camera doesn't work, try different camera IDs:



```



1  



```



### Model Not Found



If a model is not found, make sure you've downloaded it to one of the configured directories:



```



```



### Performance Issues



For better performance with limited resources, use the smaller models (e.g., yolov8n.pt instead of yolov8x.pt)