DocuMind MCP Server

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.

🌐 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

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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security – no known vulnerabilities
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license - not found
A
quality - confirmed to work

local-only server

The server can only run on the client's local machine because it depends on local resources.

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

  1. ⚡ Core Systems
    1. 🚀 System Boot Sequence
      1. System Requirements
      2. Initialize Core
      3. Compile Matrix
      4. Neural Development Link
    2. 🛸 Operation Protocol
      1. System Configuration
      2. Neural Interface Commands
    3. 🔮 Development Matrix
      1. Debug Protocol
      2. Troubleshooting Guide
    4. 🔬 API Documentation
      1. Core Classes
      2. Core Interfaces
      3. Error Handling
    5. ⚡ License

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