Video Still Capture MCP Server

What does “Video Still Capture” MCP Server do?

Video Still Capture MCP is a Python-based Model Context Protocol (MCP) server designed to provide AI assistants with seamless access and control over webcams and video sources using OpenCV. This server exposes tools that allow language models and AI agents to capture images, manage video connections, and manipulate camera settings like brightness, contrast, and resolution. It enhances development workflows by enabling AI-driven tasks such as on-demand photo capture, basic image processing (e.g., horizontal flipping), and camera property adjustments, all through standardized MCP interfaces. This makes it especially useful in scenarios where visual context or real-world image data is required for AI tasks, automation, or user interactions.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

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List of Resources

No explicit MCP resources are mentioned in the repository or documentation.

List of Tools

• quick_capture
  Captures a single image from a webcam or video source without the need to manage persistent connections. Allows AI agents to quickly grab a still image from an OpenCV-compatible device.

Other tools may exist, but only quick_capture is referenced in the available documentation.

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Use Cases of this MCP Server

• On-demand Image Capture
  Allows developers or AI agents to take a real-time photo from a webcam for use in visual analysis, documentation, or user interaction.
• Camera Settings Adjustment
  Enables programmatic modification of camera properties such as brightness, contrast, and resolution, facilitating adaptable imaging conditions.
• Image Processing
  Supports simple transformations like horizontal flipping, making it easy to preprocess images for downstream tasks.
• Experimentation with AI Vision
  Makes it straightforward for developers to incorporate real-world visual data into AI workflows, such as object detection or scene understanding.
• Webcam Connection Management
  Provides tools to open, manage, and close camera connections programmatically, supporting dynamic use in larger automation systems.

How to set it up

Windsurf

No setup instructions for Windsurf are provided.

Claude

macOS/Linux

1. Ensure prerequisites: Python 3.10+, OpenCV (opencv-python), MCP Python SDK, UV (optional).
2. Clone the repository and install:

   git clone https://github.com/13rac1/videocapture-mcp.git
   cd videocapture-mcp
   pip install -e .
   

3. Edit your Claude Desktop configuration file:
   • Mac: nano ~/Library/Application\ Support/Claude/claude_desktop_config.json
   • Linux: nano ~/.config/Claude/claude_desktop_config.json
4. Add the MCP server configuration:

   {
     "mcpServers": {
       "VideoCapture": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "mcp[cli]",
           "--with",
           "numpy",
           "--with",
           "opencv-python",
           "mcp",
           "run",
           "/ABSOLUTE_PATH/videocapture_mcp.py"
         ]
       }
     }
   }
   

5. Replace /ABSOLUTE_PATH/videocapture-mcp with the absolute path to the project.
6. Restart Claude Desktop and verify that the MCP server is accessible.

Windows

1. Ensure prerequisites are installed.
2. Edit the configuration:

   nano $env:AppData\Claude\claude_desktop_config.json
   

3. Add the configuration:

   {
     "mcpServers": {
       "VideoCapture": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "mcp[cli]",
           "--with",
           "numpy",
           "--with",
           "opencv-python",
           "mcp",
           "run",
           "C:\\ABSOLUTE_PATH\\videocapture-mcp\\videocapture_mcp.py"
         ]
       }
     }
   }
   

4. Replace C:\ABSOLUTE_PATH\videocapture-mcp appropriately.
5. Restart Claude Desktop and verify.

Alternative Installation Command

• Run:

  mcp install videocapture_mcp.py
  

  This will automatically configure Claude Desktop to use Video Still Capture MCP.

Cursor

No setup instructions for Cursor are provided.

Cline

No setup instructions for Cline are provided.

Securing API Keys

No information on API key or environment variable security is provided in the documentation.

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:

{
  "VideoCapture": {
    "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 “VideoCapture” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

Video Still Capture MCP is a focused, well-defined MCP server for webcam image capture, with clear documentation for Claude integration and a straightforward tool interface. However, it currently lacks prompt templates, resource primitives, and broader platform setup or security documentation. The single-tool approach is effective for its purpose but limits extensibility.

MCP Score

Rating: 4/10
The server does its job well for image capture, but is limited in scope, missing advanced MCP features, resource documentation, and multi-platform setup guidance.