MCP Visual Data Interpretation Utility

This module establishes an MCP endpoint dedicated to processing and interpreting image data utilizing advanced generative vision capabilities accessible through the OpenRouter platform. It abstracts the complexities of model invocation for models such as Anthropic's Claude-3.5-Sonnet and Claude-3-Opus into a straightforward communication channel.

Setup Procedure

Installation requires Node Package Manager:

npm install @catalystneuro/mcp_read_images

Configuration Prerequisites

A valid API credential from OpenRouter is mandatory. Credentials can be obtained at OpenRouter Key Portal.

Integrate this service definition into your primary MCP configuration file (typically found in VSCode's global storage path, e.g., ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json):

{
  "mcpServers": {
    "vision_service": { 
      "command": "read_images",
      "env": {
        "OPENROUTER_API_KEY": "your-secure-api-key",
        "OPENROUTER_MODEL": "anthropic/claude-3.5-sonnet"  // Default model selection; optional
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Operational Guide

The server exposes a singular primary function, interpret_visual_data, for querying image content:

// Standard invocation utilizing configuration defaults
use_mcp_tool({
  server_name: "vision_service",
  tool_name: "interpret_visual_data",
  arguments: {
    image_path: "/absolute/path/to/visual_asset.png",
    question: "Provide a detailed analysis of the visual elements present."
  }
});

// Overriding the model explicitly for a high-fidelity task
use_mcp_tool({
  server_name: "vision_service",
  tool_name: "interpret_visual_data",
  arguments: {
    image_path: "/absolute/path/to/visual_asset.png",
    question: "What is the primary action depicted?",
    model: "anthropic/claude-3-opus-20240229"  // Directly supersedes all other settings
  }
});

Model Resolution Hierarchy

Model selection follows a strict precedence rule: 1. Explicit model specification within the tool invocation parameters. 2. The OPENROUTER_MODEL environment setting defined in the MCP configuration. 3. The built-in fallback model: anthropic/claude-3.5-sonnet.

Validated Visual Engines

The utility has been successfully tested with the following OpenRouter endpoints: - anthropic/claude-3.5-sonnet - anthropic/claude-3-opus-20240229

Key Capabilities

• Automated preprocessing including image scaling and compression optimization.
• Flexible selection of backend vision models.
• Support for user-defined analytical prompts.
• Delivery of explicit, actionable diagnostic feedback.
• Built-in routines for JPEG standardization and quality control.

Troubleshooting and Error Reporting

The service incorporates robust mechanisms to address common failures, providing informative feedback for:

• Invalid file system references for images.
• Missing or unauthenticated API credentials.
• Network connectivity disruptions.
• Requests targeting unsupported or misconfigured models.
• Internal issues during image manipulation.

Every failure results in a clear, actionable message aimed at swift issue resolution.

Building from Source

To compile the module from its repository:

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

Legal Information

Distributed under the terms of the MIT License. Full details are available in the LICENSE file.

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