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How to Analyze Your Repository with DocuMCP

This guide walks you through using DocuMCP's repository analysis capabilities to understand your project's documentation needs.

What Repository Analysis Provides​

DocuMCP's analysis examines your project from multiple perspectives:

  • Project Structure: File organization, language distribution, directory structure
  • Dependencies: Package ecosystems, frameworks, and libraries in use
  • Documentation Status: Existing documentation files, README quality, coverage gaps
  • Complexity Assessment: Project size, team size estimates, maintenance requirements
  • Recommendations: Tailored suggestions based on your project characteristics

Basic Analysis​

Simple Analysis Request​

analyze my repository

This performs a standard-depth analysis covering all key aspects of your project.

Specify Analysis Depth​

analyze my repository with deep analysis

Available depth levels:

  • quick: Fast overview focusing on basic structure and languages
  • standard: Comprehensive analysis including dependencies and documentation (recommended)
  • deep: Detailed analysis with advanced insights and recommendations

Understanding Analysis Results​

Project Structure Section​

{
  "structure": {
    "totalFiles": 2034,
    "totalDirectories": 87,
    "languages": {
      ".ts": 86,
      ".js": 13,
      ".css": 3,
      ".html": 37
    },
    "hasTests": true,
    "hasCI": true,
    "hasDocs": true
  }
}

This tells you:

  • Scale of your project (file/directory count)
  • Primary programming languages
  • Presence of tests, CI/CD, and existing documentation

Dependencies Analysis​

{
  "dependencies": {
    "ecosystem": "javascript",
    "packages": ["@modelcontextprotocol/sdk", "zod", "typescript"],
    "devPackages": ["jest", "@types/node", "eslint"]
  }
}

This reveals:

  • Primary package ecosystem (npm, pip, cargo, etc.)
  • Key runtime dependencies
  • Development and tooling dependencies

Documentation Assessment​

{
  "documentation": {
    "hasReadme": true,
    "hasContributing": true,
    "hasLicense": true,
    "existingDocs": ["README.md", "docs/api.md"],
    "estimatedComplexity": "complex"
  }
}

This shows:

  • Presence of essential documentation files
  • Existing documentation structure
  • Complexity level for documentation planning

Advanced Analysis Techniques​

Target Specific Directories​

analyze the src directory for API documentation needs

Focus on Documentation Gaps​

what documentation is missing from my project?

Analyze for Specific Use Cases​

analyze my repository to determine if it needs user guides or developer documentation

Using Analysis Results​

For SSG Selection​

After analysis, use the results to get targeted recommendations:

based on the analysis, what static site generator works best for my TypeScript project?

For Documentation Planning​

Use analysis insights to plan your documentation structure:

given my project complexity, how should I organize my documentation?

For Deployment Strategy​

Let analysis guide your deployment approach:

considering my project setup, what's the best way to deploy documentation?

Analysis-Driven Workflows​

Complete Documentation Setup​

  1. Analyze: analyze my repository for documentation needs
  2. Plan: Use analysis results to understand project characteristics
  3. Recommend: recommend documentation tools based on the analysis
  4. Implement: set up documentation based on the recommendations

Documentation Audit​

  1. Current State: analyze my existing documentation structure
  2. Gap Analysis: what documentation gaps exist in my project?
  3. Improvement Plan: how can I improve my current documentation?

Migration Planning​

  1. Legacy Analysis: analyze my project's current documentation approach
  2. Modern Approach: what modern documentation tools would work better?
  3. Migration Strategy: how should I migrate from my current setup?

Interpreting Recommendations​

Project Type Classification​

Analysis categorizes your project as:

  • library: Reusable code packages requiring API documentation
  • application: End-user software needing user guides and tutorials
  • tool: Command-line or developer tools requiring usage documentation

Team Size Estimation​

  • small: 1-3 developers, favor simple solutions
  • medium: 4-10 developers, need collaborative features
  • large: 10+ developers, require enterprise-grade solutions

Complexity Assessment​

  • simple: Basic projects with minimal documentation needs
  • moderate: Standard projects requiring structured documentation
  • complex: Large projects needing comprehensive documentation strategies

Common Analysis Patterns​

JavaScript/TypeScript Projects​

Analysis typically reveals:

  • npm ecosystem with extensive dev dependencies
  • Need for API documentation (if library)
  • Integration with existing build tools
  • Recommendation: Often Docusaurus or VuePress

Python Projects​

Analysis usually shows:

  • pip/poetry ecosystem
  • Sphinx-compatible documentation needs
  • Strong preference for MkDocs
  • Integration with Python documentation standards

Multi-Language Projects​

Analysis identifies:

  • Mixed ecosystems and dependencies
  • Need for language-agnostic solutions
  • Recommendation: Usually Hugo or Jekyll for flexibility

Troubleshooting Analysis​

Incomplete Results​

If analysis seems incomplete:

run deep analysis on my repository to get more detailed insights

Focus on Specific Areas​

If you need more details about certain aspects:

analyze my project's dependencies in detail

Re-analyze After Changes​

After making significant changes:

re-analyze my repository to see updated recommendations

Analysis Memory and Caching​

DocuMCP stores analysis results for reference in future operations:

  • Analysis IDs are provided for referencing specific analyses
  • Results remain accessible throughout your session
  • Memory system learns from successful documentation deployments

Use analysis IDs in follow-up requests:

using analysis analysis_abc123, set up the recommended documentation structure

Best Practices​

  1. Start Fresh: Begin new documentation projects with analysis
  2. Regular Reviews: Re-analyze periodically as projects evolve
  3. Deep Dive When Needed: Use deep analysis for complex projects
  4. Combine with Expertise: Use analysis as a starting point, not final decision
  5. Iterate: Refine based on analysis feedback and results

Analysis is the foundation of effective documentation planning with DocuMCP. Use it to make informed decisions about tools, structure, and deployment strategies.