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Getting Started with DocuMCP

This tutorial will guide you through setting up and using DocuMCP's intelligent documentation deployment system with memory-enhanced capabilities.

Prerequisites​

Before you begin, ensure you have:

  • Node.js 16+ installed
  • Access to a GitHub repository
  • Claude Code or another MCP-compatible client
  • Basic familiarity with documentation workflows

🎯 Pro Tip: Reference LLM_CONTEXT.md​

When using DocuMCP through an AI assistant, always reference the LLM_CONTEXT.md file for comprehensive tool context:

@LLM_CONTEXT.md help me set up documentation for my TypeScript project

The LLM_CONTEXT.md file is auto-generated and contains:

  • All 45 tool descriptions and parameters
  • Usage examples and workflows
  • Memory system documentation
  • Phase 3 code-to-docs sync features

Location: /LLM_CONTEXT.md (in project root)

This ensures your AI assistant has complete context and can provide optimal recommendations.

Step 1: Initial Repository Analysis​

Start by analyzing your repository to understand its characteristics and documentation needs:

{
  "path": "/path/to/your/project",
  "depth": "standard"
}

This will analyze your project and return:

  • Project structure: File counts, languages used, and organization
  • Dependencies: Production and development packages detected
  • Documentation status: Existing docs, README, contributing guidelines
  • Smart recommendations: Primary language, project type, team size estimates
  • Unique analysis ID: For use in subsequent steps

Example response snippet:

{
  "id": "analysis_abc123xyz",
  "structure": {
    "totalFiles": 150,
    "languages": { ".ts": 45, ".js": 12, ".md": 8 },
    "hasTests": true,
    "hasCI": true
  },
  "dependencies": {
    "ecosystem": "javascript",
    "packages": ["react", "typescript"]
  },
  "recommendations": {
    "primaryLanguage": "typescript",
    "projectType": "library"
  }
}

Step 2: Memory-Enhanced SSG Recommendation​

Next, get intelligent recommendations powered by DocuMCP's memory system:

{
  "analysisId": "analysis_abc123xyz",
  "preferences": {
    "ecosystem": "javascript",
    "priority": "features"
  }
}

The memory system leverages patterns from 130+ previous projects to provide:

  • Confidence-scored recommendations (e.g., Docusaurus with 85% confidence)
  • Historical success data (69% deployment success rate insights)
  • Pattern-based insights (Hugo most common with 98 projects, but Docusaurus optimal for TypeScript)
  • Similar project examples to learn from successful configurations

Example recommendation response:

{
  "recommended": "docusaurus",
  "confidence": 0.85,
  "reasoning": [
    "JavaScript/TypeScript ecosystem detected",
    "Modern React-based framework aligns with project stack",
    "Strong community support and active development"
  ],
  "alternatives": [
    {
      "name": "MkDocs",
      "score": 0.75,
      "pros": ["Simple setup", "Great themes"],
      "cons": ["Limited React component support"]
    }
  ]
}

Step 3: Configuration Generation​

Generate optimized configuration files for your chosen SSG:

// Generate Docusaurus configuration
{
  "ssg": "docusaurus",
  "projectName": "Your Project",
  "projectDescription": "Your project description",
  "outputPath": "/path/to/your/repository"
}

Step 4: Diataxis Structure Setup​

Create a professional documentation structure following the Diataxis framework:

// Setup documentation structure
{
  "path": "/path/to/your/repository/docs",
  "ssg": "docusaurus",
  "includeExamples": true
}

This creates four optimized sections:

  • Tutorials: Learning-oriented guides
  • How-to Guides: Problem-solving instructions
  • Reference: Information-oriented technical docs
  • Explanation: Understanding-oriented background

Step 5: GitHub Pages Deployment​

Set up automated deployment with security best practices:

// Deploy to GitHub Pages
{
  "repository": "/path/to/your/repository",
  "ssg": "docusaurus",
  "branch": "gh-pages"
}

This generates:

  • GitHub Actions workflow with OIDC authentication
  • Minimal security permissions (pages:write, id-token:write only)
  • Automated build and deployment pipeline

Step 6: Memory System Exploration​

Explore DocuMCP's advanced memory capabilities:

// Get learning statistics
{
  "includeDetails": true
}

// Recall similar projects
{
  "query": "typescript documentation",
  "type": "recommendation",
  "limit": 5
}

The memory system provides:

  • Pattern Recognition: Most successful SSG choices for your project type
  • Historical Insights: Success rates and common issues
  • Smart Recommendations: Enhanced suggestions based on similar projects

Verification​

Verify your setup with these checks:

  1. Documentation Structure: Confirm all Diataxis directories are created
  2. Configuration Files: Check generated config files are valid
  3. GitHub Actions: Verify workflow file in .github/workflows/
  4. Memory Insights: Review recommendations and confidence scores

Summary​

In this tutorial, you learned how to:

  • Analyze repositories with comprehensive project profiling
  • Get intelligent SSG recommendations using memory-enhanced insights
  • Generate optimized configurations for your chosen static site generator
  • Create Diataxis-compliant structures for professional documentation
  • Set up automated GitHub Pages deployment with security best practices
  • Leverage the memory system for enhanced recommendations and insights

Next Steps​