documind

DocuMind MCP Server provides an AI-assisted analysis pipeline for documentation quality. It performs neural processing to evaluate README-like materials, scans SVG header images for quality metrics, and supports multi-language verification to ensure badges and structure meet recognizable standards. The server exposes an evaluation workflow that can be triggered to analyze a project’s documentation and return structured feedback, including scores and improvement suggestions. Users can interact with the Neural Interface Commands to initiate evaluation tasks and receive detailed results about header images, language badge configurations, and overall documentation quality. The server is designed to be integrated with external configuration systems (e.g., Claude Desktop) and can be inspected via the MCP Inspector for debugging and performance tuning.

Prerequisites:

• Node.js 18+ installed on your system
• npm or yarn available in PATH

Installation steps:

1. Clone or download the DocuMind MCP Server repository
2. Navigate to the project root
3. Install dependencies: npm install
4. Build the server (compiles TypeScript/ prepares runtime artifacts): npm run build
5. (Optional for active development) Start in watch mode to rebuild on changes: npm run watch
6. Start the server (if a local runtime is desired): node build/index.js

Configuration notes:

• The server can be wired into external configuration like Claude Desktop by pointing to the built index.js entry (build/index.js).
• Ensure environment variables (e.g., NODE_ENV, LOG_LEVEL) are set as needed for your deployment environment.

Tips and common considerations:

• Ensure the header SVGs and badges follow recommended formats to avoid false negatives in image analysis.
• If you encounter build errors, clear node_modules and reinstall dependencies, and verify TypeScript/tooling versions align with project requirements.
• For MCP integration, the server’s entry point should be reachable by the host (e.g., build/index.js). Use an absolute path in production configurations if necessary.
• If you customize evaluation parameters or paths, ensure the projectPath provided to evaluate_readme is accessible and correctly mounted in your deployment environment.
• The inspector (npm run inspector) can help debug the neural network interactions and performance metrics during development.