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Sunwood-ai-labs_documind-mcp-server
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Sunwood-ai-labs_documind-mcp-server

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2024-12-17

Mirror ofhttps://github.com/Sunwood-ai-labs/documind-mcp-server

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DocuMind MCP Server
EN doc JA doc

🌐 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

🚀 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

  1. Header Image Not Detected

    • Ensure SVG file is placed in the assets/ directory
    • Validate SVG file contains proper XML structure
    • Check file permissions
  2. Language Badges Not Recognized

    • Verify badges use shields.io format
    • Check HTML structure follows recommended pattern
    • Ensure proper center alignment
  3. Build Errors

    • Clear node_modules and reinstall dependencies
    • Ensure TypeScript version matches project requirements
    • Check for syntax errors in modified files
  4. MCP Connection Issues

    • Verify stdio transport configuration
    • Check Claude Desktop configuration
    • Ensure proper file paths in config

Performance Optimization

  1. SVG Analysis

    • Minimize SVG complexity for faster parsing
    • Use efficient gradients and animations
    • Optimize file size while maintaining quality
  2. 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.

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    Reviews

    1 (1)
    Avatar
    user_lfhrfLtX
    2025-04-15

    The Model Context Protocol (MCP) Server by RonTrace has significantly improved our project management efficiency. Its intuitive interface and robust performance are unparalleled. The seamless integration with various AI protocols has been a game-changer. Highly recommend for teams looking to streamline their workflows. Check it out at https://mcp.so/server/ai-rules-mcp/RonTrace.