DocuMind MCP Server

local-only server

The server can only run on the clientโ€™s local machine because it depends on local resources.

Integrations

  • Performs comprehensive analysis of markdown documentation structure, providing scoring and optimization suggestions.

  • Evaluates and validates shields.io badge formats in documentation, ensuring proper implementation of language badges and other project indicators.

  • Analyzes SVG header images for quality metrics including gradients, animations, rounded corners, and project-specific elements.

๐ŸŒ 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.

โšก Core Systems

  • ๐Ÿง  Neural Documentation Analysis: Advanced algorithms for comprehensive README evaluation
  • ๐Ÿ”ฎ Holographic Header Scanning: Cutting-edge SVG analysis for visual elements
  • ๐ŸŒ Multi-dimensional Language Support: Cross-linguistic documentation verification
  • ๐Ÿ’ซ Quantum Suggestion Engine: AI-powered improvement recommendations

๐Ÿš€ System Boot Sequence

System Requirements

  • Node.js 18+
  • npm || yarn

Initialize Core

npm install

Compile Matrix

npm run build

Establish real-time neural connection:

npm run watch

๐Ÿ›ธ 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 ] }) } ] }

๐Ÿ”ฎ 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

๐Ÿ”ฌ 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 }; }

โšก License

Operating under MIT Protocol.

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security โ€“ no known vulnerabilities
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license - not found
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quality - confirmed to work

A Model Context Protocol server that analyzes and evaluates GitHub README documentation quality using advanced neural processing, providing scores and improvement suggestions.

  1. โšก Core Systems
    1. ๐Ÿš€ System Boot Sequence
      1. System Requirements
      2. Initialize Core
      3. Compile Matrix
      4. Neural Development Link
    2. ๐Ÿ›ธ Operation Protocol
      1. System Configuration
      2. Neural Interface Commands
    3. ๐Ÿ”ฎ Development Matrix
      1. Debug Protocol
      2. Troubleshooting Guide
    4. ๐Ÿ”ฌ API Documentation
      1. Core Classes
      2. Core Interfaces
      3. Error Handling
    5. โšก License