Moondream

You run the Moondream MCP Server to add advanced image analysis capabilities to your MCP-enabled assistants. It connects your applications to the Moondream vision model, enabling image captioning, object detection, and visual question answering with efficient 8-bit quantized inference. This setup handles model downloading and environment preparation automatically, so you can focus on building powerful AI-assisted features.

How to use

You use the server by loading it as an MCP tool in your MCP client. The server exposes an image analysis tool that can caption images, detect objects, and answer questions about image content. You can combine this with your existing MCP workflow to extract descriptions, verify object presence, or respond to visual queries in natural language.

How to install

Prerequisites include Node.js v18 or higher and Python 3.8+. Ensure the UV package manager is installed or will be installed automatically during setup.

# 1. Clone and setup
git clone <repository-url>
cd moondream-server
pnpm install

# 2. Build the server
pnpm run build

Configuration and integration

To integrate with Claude or Cline, add the MCP server configuration to your MCP settings. The following is the exact snippet you should place in your MCP settings file to run the Moondream server locally via Node.js.

{
  "mcpServers": {
    "moondream": {
      "command": "node",
      "args": ["/path/to/moondream-server/build/index.js"]
    }
  }
}

Notes on running and startup

The server startup flow progresses from building to launching the Node-based server script. If you run into port conflicts, the default port is 3475 and you can identify conflicts with system process checks.

Troubleshooting

Model download issues can arise if a local download fails. Ensure network access and retry the build/start steps. If you encounter a Python environment problem, UV manages dependencies and you can review logs in the temporary directory. The virtual environment is typically created in the system temp folder.

Examples of common usage

Example workflows include generating captions for a given image, detecting a specific object in a scene, or asking questions such as what colors are used in a painting.

Available tools

analyze_image

Powerful image analysis tool with modes for generating captions, detecting objects, and answering image questions. Examples include caption generation, object detection like detecting a person or car, and visual question answering.