documcp

# 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 ```json { "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 ```json { "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 ```json { "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.