OpenCV MCP Server

What does “OpenCV” MCP Server do?

The OpenCV MCP Server provides OpenCV’s image and video processing capabilities through the Model Context Protocol (MCP). It acts as a bridge, enabling AI assistants and developer tools to access advanced computer vision functionalities. This server allows seamless execution of tasks such as basic image manipulation, object detection, and visual tracking by exposing OpenCV tools and workflows via a standardized protocol. By integrating with external data sources, APIs, or services, it empowers developers to build richer, context-aware AI-powered applications and automations that leverage the full potential of OpenCV from within their preferred development environments.

List of Prompts

No prompt templates are explicitly listed in the repository or documentation.

List of Resources

No explicit resources are listed in the repository or documentation.

List of Tools

No detailed tool list is provided in the repository or documentation. However, the description suggests exposure of image and video processing capabilities, basic image manipulation, and object detection tools.

Use Cases of this MCP Server

• Image Manipulation: Automate image resizing, cropping, and filtering tasks directly from your development environment.
• Object Detection: Integrate object detection capabilities into your AI workflows, enabling identification and localization of objects within images or video streams.
• Video Processing: Perform frame extraction, video analysis, or tracking operations for computer vision projects.
• AI-Powered Automation: Use OpenCV tools in conjunction with LLMs for tasks such as automated document analysis, smart surveillance, or quality inspection.
• Data Augmentation: Enhance datasets for machine learning by programmatically transforming images and videos using OpenCV’s robust suite of functions.

How to set it up

Windsurf

1. Ensure you have Node.js and the Windsurf platform installed.
2. Open your Windsurf configuration file.
3. Add the OpenCV MCP Server to the mcpServers section using the following JSON snippet:

   {
     "opencv-mcp": {
       "command": "npx",
       "args": ["@opencv/mcp-server@latest"]
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify that the OpenCV MCP Server is listed and accessible.

Claude

1. Install Node.js and ensure Claude is set up.
2. Locate the Claude configuration file.
3. Insert the OpenCV MCP Server into the mcpServers array:

   {
     "opencv-mcp": {
       "command": "npx",
       "args": ["@opencv/mcp-server@latest"]
     }
   }
   

4. Save changes and restart Claude.
5. Check server status within Claude’s interface.

Cursor

1. Make sure Node.js and Cursor are installed.
2. Find and open the Cursor configuration file.
3. Add the following under mcpServers:

   {
     "opencv-mcp": {
       "command": "npx",
       "args": ["@opencv/mcp-server@latest"]
     }
   }
   

4. Save and restart Cursor.
5. Confirm the OpenCV MCP Server is running.

Cline

1. Confirm Node.js and Cline installation.
2. Access the Cline config file.
3. Add this snippet to your MCP servers list:

   {
     "opencv-mcp": {
       "command": "npx",
       "args": ["@opencv/mcp-server@latest"]
     }
   }
   

4. Save and restart Cline.
5. Verify connection in the Cline UI.

Securing API Keys

Store sensitive API keys in environment variables instead of configuration files. Reference them in your configuration as shown:

{
  "opencv-mcp": {
    "command": "npx",
    "args": ["@opencv/mcp-server@latest"],
    "env": {
      "API_KEY": "${OPENCV_API_KEY}"
    },
    "inputs": {
      "apiKey": "${OPENCV_API_KEY}"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "opencv-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “opencv-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the available information, the OpenCV MCP Server provides a clear overview and setup guidance, but lacks public documentation on prompt templates, explicit resources, and detailed tool definitions. For developers seeking computer vision capabilities in MCP, it offers value, but would benefit from richer documentation and examples.

MCP Score

Overall, I would rate this MCP server a 4/10 based on current visibility: it is open source, clearly scoped for OpenCV tasks, but lacks detailed documentation on tools, prompts, and resources needed for advanced or transparent integration.