The DocuMind MCP Server is an advanced documentation quality analysis system that:

Evaluates README Quality: Analyzes structure and content of README files within a project
Analyzes Visual Elements: Assesses SVG header images for features like gradients, animations, rounded corners, and project specificity
Language Support: Verifies documentation across multiple languages and evaluates language badges
Generates Scores: Provides quantitative scoring of documentation quality
Offers Improvement Suggestions: Provides AI-powered recommendations to enhance documentation
Supports Integration: Can integrate with Claude Desktop mainframe and offers API access
Cross-platform Compatibility: Runs on Node.js 18+ with npm/yarn support
Developer-Friendly: Includes real-time development support in watch mode

🌐 DocuMind MCP Server

"Where Documentation Meets Digital Intelligence"

A next-generation Model Context Protocol (MCP) server that revolutionizes documentation quality analysis through advanced neural processing.

⚡ Core Systems

🧠 Neural Documentation Analysis: Advanced algorithms for comprehensive README evaluation
🔮 Holographic Header Scanning: Cutting-edge SVG analysis for visual elements
🌍 Multi-dimensional Language Support: Cross-linguistic documentation verification
💫 Quantum Suggestion Engine: AI-powered improvement recommendations

Related MCP server: GitHub MCP Server

🚀 System Boot Sequence

System Requirements

Node.js 18+
npm || yarn

Initialize Core

npm install

Compile Matrix

npm run build

Neural Development Link

Establish real-time neural connection:

npm run watch

🛸 Operation Protocol

System Configuration

Integrate with Claude Desktop mainframe:

Windows Terminal:

// %APPDATA%/Claude/claude_desktop_config.json { "mcpServers": { "documind-mcp-server": { "command": "/path/to/documind-mcp-server/build/index.js" } } }

Neural Interface Commands

evaluate_readme

Initiates quantum analysis of documentation structure.

Parameters:

projectPath: Neural pathway to target directory

Example Request:

{ name: "evaluate_readme", arguments: { projectPath: "/path/to/project" } }

Example Response:

{ content: [ { type: "text", text: JSON.stringify({ filePath: "/path/to/project/README.md", hasHeaderImage: true, headerImageQuality: { hasGradient: true, hasAnimation: true, // ... other quality metrics }, score: 95, suggestions: [ "Consider adding language badges", // ... other suggestions ] }) } ] }

🔮 Development Matrix

Debug Protocol

Access the neural network through MCP Inspector:

npm run inspector

Troubleshooting Guide

Common Issues and Solutions

Header Image Not Detected
- Ensure SVG file is placed in the assets/ directory
- Validate SVG file contains proper XML structure
- Check file permissions
Language Badges Not Recognized
- Verify badges use shields.io format
- Check HTML structure follows recommended pattern
- Ensure proper center alignment
Build Errors
- Clear node_modules and reinstall dependencies
- Ensure TypeScript version matches project requirements
- Check for syntax errors in modified files
MCP Connection Issues
- Verify stdio transport configuration
- Check Claude Desktop configuration
- Ensure proper file paths in config

Performance Optimization

SVG Analysis
- Minimize SVG complexity for faster parsing
- Use efficient gradients and animations
- Optimize file size while maintaining quality
README Scanning
- Structure content for optimal parsing
- Use recommended markdown patterns
- Follow badge placement guidelines

🔬 API Documentation

Core Classes

ReadmeService

Primary service for README analysis and evaluation.

class ReadmeService { // Analyzes all README files in a project async evaluateAllReadmes(projectPath: string): Promise<ReadmeEvaluation[]> // Evaluates a single README file private async evaluateReadme(dirPath: string, readmePath: string): Promise<ReadmeEvaluation> // Evaluates language badge configuration private evaluateLanguageBadges(content: string): BadgeEvaluation }

SVGService

Specialized service for SVG header image analysis.

class SVGService { // Evaluates SVG header image quality public evaluateHeaderImageQuality(imgSrc: string, content: string): HeaderImageQuality // Checks for project-specific elements in SVG private checkProjectSpecificImage(svgContent: string, readmeContent: string): boolean }

Core Interfaces

interface ReadmeEvaluation { filePath: string; hasHeaderImage: boolean; headerImageQuality: HeaderImageQuality; isCentered: { headerImage: boolean; title: boolean; badges: boolean; }; hasBadges: { english: boolean; japanese: boolean; isCentered: boolean; hasCorrectFormat: boolean; }; score: number; suggestions: string[]; } interface HeaderImageQuality { hasGradient: boolean; hasAnimation: boolean; hasRoundedCorners: boolean; hasEnglishText: boolean; isProjectSpecific: boolean; }

Error Handling

The server implements comprehensive error handling:

try { const evaluations = await readmeService.evaluateAllReadmes(projectPath); // Process results } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [{ type: 'text', text: `Evaluation error: ${errorMessage}` }], isError: true }; }

⚡ License

Operating under MIT Protocol.

DocuMind MCP Server