MCP Read Images

# 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 ```bash npm install @catalystneuro/mcp_read_images ``` ## Configuration The server requires an OpenRouter API key. You can get one from [OpenRouter](https://openrouter.ai/keys). 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): ```json { "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: ```typescript // 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: ```bash git clone https://github.com/catalystneuro/mcp_read_images.git cd mcp_read_images npm install npm run build ``` ## License MIT License. See [LICENSE](LICENSE) for details.