Video Capture MCP

Video Capture MCP

What is Video Capture MCP about?

Video Capture MCP provides a Model Context Protocol (MCP) server that lets AI assistants interact with webcams or video sources through OpenCV. It offers tools for quick image capture, persistent camera connections, property manipulation, and basic image transformations.

How to use Video Capture MCP?

1. Installation

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

2. Run the server

   mcp dev videocapture_mcp.py
   

3. Integrate with an AI client (e.g., Claude Desktop) by adding a server entry in the client’s claude_desktop_config.json that points to the absolute path of videocapture_mcp.py.

Key features of Video Capture MCP

• quick_capture: Open a camera, capture one frame, and close it.
• open_camera / close_connection: Manage persistent camera connections.
• capture_frame: Grab a frame from an existing connection.
• get_video_properties / set_video_property: Read and adjust settings such as brightness, contrast, resolution, and FPS.
• list_active_connections: View all currently open video streams.
• Image flipping: Optional horizontal flip on captured frames.

Use cases of Video Capture MCP

• Enabling AI assistants to take photos on demand (e.g., “show me a picture of the desk”).
• Performing multi‑step visual workflows where several frames are needed from the same camera.
• Adjusting camera parameters programmatically to improve image quality for downstream computer‑vision tasks.
• Supporting multi‑camera setups by selecting device indexes.

FAQ

Q: Does the server stream video? A: No, it only captures still images; video streaming is not provided.

Q: Which Python version is required? A: Python 3.10 or newer.

Q: What dependencies are needed? A: opencv-python, the MCP Python SDK, and optionally uv for isolated environments.

Q: How do I close a camera to free resources? A: Call close_connection(connection_id) or rely on the server’s automatic cleanup at shutdown.

Q: Can I use this on Windows? A: Yes. Follow the Windows‑specific configuration snippet in the README to add the MCP server to Claude Desktop.

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.