Video Still Capture

What is Video Still Capture MCP?

Video Still Capture MCP is a Model Context Protocol (MCP) server designed to allow AI assistants to interact with webcams and video sources using OpenCV. It enables language models to capture images, adjust camera settings, and manage video connections, effectively bridging the gap between AI and real-world visual input.

How to use Video Still Capture MCP?

To use Video Still Capture MCP, you first need to install the prerequisites, including Python 3.10+, OpenCV, and the MCP Python SDK. You can then install the project from source by cloning the repository and running pip install -e .. The MCP server can be run using mcp dev videocapture_mcp.py. For integration with Claude for Desktop, you need to modify the Claude Desktop configuration file to include the MCP server settings, ensuring you provide the absolute path to the videocapture_mcp.py file. Alternatively, you can use the mcp install videocapture_mcp.py command for automatic configuration. Once integrated, AI assistants like Claude can be prompted to perform webcam-related tasks.

Key Features of Video Still Capture MCP

• Quick Image Capture: Capture a single image from a webcam without managing persistent connections.
• Connection Management: Open, manage, and close connections to camera devices.
• Video Properties: Read and adjust various camera settings such as brightness, contrast, and resolution.
• Image Processing: Perform basic image transformations like horizontal flipping.
• Resource Management: Automatic management of camera resources, ensuring proper release upon server shutdown.
• Multiple Camera Support: Ability to specify device index for systems with multiple cameras.

Use Cases of Video Still Capture MCP

Video Still Capture MCP is primarily used to enable AI assistants to interact with webcams. Examples include:

• Taking quick photos: An AI can be instructed to capture a single image from the webcam.
• Persistent camera access: An AI can open and maintain a connection to the webcam for multiple image captures.
• Adjusting camera settings: An AI can modify camera properties like brightness or resolution based on user requests.

FAQ about Video Still Capture MCP

• Camera Not Found: Ensure your webcam is properly connected and not in use by another application.
• Permission Issues: Some operating systems may require explicit permission for camera access.
• OpenCV Installation: If you encounter issues with OpenCV, refer to the official OpenCV installation guide for troubleshooting.

Video Still Capture MCP

A Model Context Protocol server for accessing and controlling webcams via OpenCV

Overview

Video Still Capture MCP is a Python implementation of the Model Context Protocol (MCP) that provides AI assistants with the ability to access and control webcams and video sources through OpenCV. This server exposes a set of tools that allow language models to capture images, manipulate camera settings, and manage video connections. There is no video capture.

Examples

Here are some examples of the Video Still Capture MCP server in action:

Orange Example

Left: Claude's view of the image	Right: Actual webcam capture

Magnet Example

Left: Claude's view of the image	Right: Actual webcam capture

Installation

Prerequisites

• Python 3.10+
• OpenCV (opencv-python)
• MCP Python SDK
• UV (optional)

Installation from source

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

Run the MCP server:

mcp dev videocapture_mcp.py

Integrating with Claude for Desktop

macOS/Linux

Edit your Claude Desktop configuration:

# Mac
nano ~/Library/Application\ Support/Claude/claude_desktop_config.json
# Linux
nano ~/.config/Claude/claude_desktop_config.json 

Add this MCP server configuration:

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

Ensure you replace /ABSOLUTE_PATH/videocapture-mcp with the project's absolute path.

Windows

Edit your Claude Desktop configuration:

nano $env:AppData\Claude\claude_desktop_config.json

Add this MCP server 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"
      ]
    }
  }
}

Ensure you replace C:\ABSOLUTE_PATH\videocapture-mcp with the project's absolute path.

Using the Installation Command

Alternatively, you can use the mcp CLI to install the server:

mcp install videocapture_mcp.py

This will automatically configure Claude Desktop to use your videocapture MCP server.

Once integrated, Claude will be able to access your webcam or video source when requested. Simply ask Claude to take a photo or perform any webcam-related task.

Features

• Quick Image Capture: Capture a single image from a webcam without managing connections
• Connection Management: Open, manage, and close camera connections
• Video Properties: Read and adjust camera settings like brightness, contrast, and resolution
• Image Processing: Basic image transformations like horizontal flipping

Tools Reference

quick_capture

Quickly open a camera, capture a single frame, and close it.

quick_capture(device_index: int = 0, flip: bool = False) -> Image

• device_index: Camera index (0 is usually the default webcam)
• flip: Whether to horizontally flip the image
• Returns: The captured frame as an Image object

open_camera

Open a connection to a camera device.

open_camera(device_index: int = 0, name: Optional[str] = None) -> str

• device_index: Camera index (0 is usually the default webcam)
• name: Optional name to identify this camera connection
• Returns: Connection ID for the opened camera

capture_frame

Capture a single frame from the specified video source.

capture_frame(connection_id: str, flip: bool = False) -> Image

• connection_id: ID of the previously opened video connection
• flip: Whether to horizontally flip the image
• Returns: The captured frame as an Image object

get_video_properties

Get properties of the video source.

get_video_properties(connection_id: str) -> dict

• connection_id: ID of the previously opened video connection
• Returns: Dictionary of video properties (width, height, fps, etc.)

set_video_property

Set a property of the video source.

set_video_property(connection_id: str, property_name: str, value: float) -> bool

• connection_id: ID of the previously opened video connection
• property_name: Name of the property to set (width, height, brightness, etc.)
• value: Value to set
• Returns: True if successful, False otherwise

close_connection

Close a video connection and release resources.

close_connection(connection_id: str) -> bool

• connection_id: ID of the connection to close
• Returns: True if successful

list_active_connections

List all active video connections.

list_active_connections() -> list

• Returns: List of active connection IDs

Example Usage

Here's how an AI assistant might use the Webcam MCP server:

1. Take a quick photo:

   I'll take a photo using your webcam.
   

   (The AI would call quick_capture() behind the scenes)

2. Open a persistent connection:

   I'll open a connection to your webcam so we can take multiple photos.
   

   (The AI would call open_camera() and store the connection ID)

3. Adjust camera settings:

   Let me increase the brightness of the webcam feed.
   

   (The AI would call set_video_property() with the appropriate parameters)

Advanced Usage

Resource Management

The server automatically manages camera resources, ensuring all connections are properly released when the server shuts down. For long-running applications, it's good practice to explicitly close connections when they're no longer needed.

Multiple Cameras

If your system has multiple cameras, you can specify the device index when opening a connection:

# Open the second webcam (index 1)
connection_id = open_camera(device_index=1)

Troubleshooting

• Camera Not Found: Ensure your webcam is properly connected and not in use by another application
• Permission Issues: Some systems require explicit permission to access the camera
• OpenCV Installation: If you encounter issues with OpenCV, refer to the official installation guide

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.