MCP Read Images

An MCP server for analyzing images using OpenRouter vision models. This server provides a simple interface to analyze images using various vision models like Claude-3.5-sonnet and Claude-3-opus through the OpenRouter API.

Installation

npm install @catalystneuro/mcp_read_images

Configuration

The server requires an OpenRouter API key. You can get one from OpenRouter.

Add the server to your MCP settings file (usually located at ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json for VSCode):

{
  "mcpServers": {
    "read_images": {
      "command": "read_images",
      "env": {
        "OPENROUTER_API_KEY": "your-api-key-here",
        "OPENROUTER_MODEL": "anthropic/claude-3.5-sonnet"  // optional, defaults to claude-3.5-sonnet
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Usage

The server provides a single tool analyze_image that can be used to analyze images:

// Basic usage with default model
use_mcp_tool({
  server_name: "read_images",
  tool_name: "analyze_image",
  arguments: {
    image_path: "/path/to/image.jpg",
    question: "What do you see in this image?"  // optional
  }
});

// Using a specific model for this call
use_mcp_tool({
  server_name: "read_images",
  tool_name: "analyze_image",
  arguments: {
    image_path: "/path/to/image.jpg",
    question: "What do you see in this image?",
    model: "anthropic/claude-3-opus-20240229"  // overrides default and settings
  }
});

Model Selection

The model is selected in the following order of precedence:

Model specified in the tool call (model argument)
Model specified in MCP settings (OPENROUTER_MODEL environment variable)
Default model (anthropic/claude-3.5-sonnet)

Supported Models

The following OpenRouter models have been tested:

anthropic/claude-3.5-sonnet
anthropic/claude-3-opus-20240229

Features

Automatic image resizing and optimization
Configurable model selection
Support for custom questions about images
Detailed error messages
Automatic JPEG conversion and quality optimization

Error Handling

The server handles various error cases:

Invalid image paths
Missing API keys
Network errors
Invalid model selections
Image processing errors

Each error will return a descriptive message to help diagnose the issue.

Development

To build from source:

git clone https://github.com/catalystneuro/mcp_read_images.git
cd mcp_read_images
npm install
npm run build

License

MIT License. See LICENSE for details.