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MCP Camera Tool Server

Provides MCP-compatible camera capture and image analysis via stdio for AI systems.

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "danson-dan-mcp_camera": {
      "command": "uv",
      "args": [
        "run",
        "mcp-camera"
      ]
    }
  }
}

MCP Camera Tool lets you connect to and control network cameras from within an MCP-enabled AI workflow. It can capture images, adjust camera parameters, perform basic image processing, and analyze image content so your AI system can reason about brightness, color distribution, edges, and more in real time.

How to use

You will start the MCP Camera Tool as an MCP server that communicates over the stdio channel. The tool exposes a capture-and-analyze image capability you can invoke from your MCP client to grab a frame from the connected camera, apply basic processing, and return analyzed metrics such as brightness, color distribution, and edges. Start the service using the standard startup variant you prefer and then issue requests from your MCP client to capture and analyze images.

How to install

Prerequisites you need on your system:

Python 3.14 or later is required for the camera tooling components.
uvx (the MCP package manager) is used to manage and run MCP services.
OpenCV is used for camera operations and image processing.
An MCP-enabled client will communicate with the server over the stdio channel or the configured MCP interface.

Step-by-step installation flow you should follow:

uv sync

# This installs dependencies and prepares the environment for the MCP Camera Tool
# Ensure camera access permissions are granted on your OS if prompted

# Start the MCP camera service (examples shown below are the two valid startup options)

uv run mcp-camera

# Or run the main Python entry directly if you prefer
uv run python main.py

Additional notes

Camera permissions may be required, especially on macOS, the first time you run the service. If prompted, grant camera access to your terminal application in Security & Privacy settings under Privacy > Camera.

Testing the camera locally is supported by a dedicated test script. You can verify the camera functionality by running the test script through the same MCP startup flow.

This tool integrates with MCP-enabled AI systems to enable image capture and analysis within your workflows. It highlights that the service uses a FastMCP-based framework and communicates via stdio, offering a capture_and_analyze_image endpoint for practical use.

Configuration and troubleshooting

If you encounter issues with camera access, verify that your camera is properly connected and that no other application is holding the camera device. Check that the required Python packages and MCP runtime are installed and that the uvx tool is available for launching MCP servers.

Project structure and entry points

Key files include the main program and core services that implement the camera handling and image analysis. Look for the following entry points and modules in your local setup to understand how the server is organized and how to extend image analysis.

Available tools

capture_and_analyze_image

Captures a frame from the connected camera, applies image processing, and returns both the image data and analyzed metrics such as brightness, color distribution, and edge information for MCP-based workflows.