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: 1. Model specified in the tool call (model argument) 2. Model specified in MCP settings (OPENROUTER_MODEL environment variable) 3. 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.