AutoDocs MCP Server

# Implementation Priorities Guide ## What Are Implementation Priorities? Implementation priorities are a strategic ordering of development tasks based on: - **Risk reduction** (tackle the hardest/most uncertain parts first) - **Value delivery** (get something working quickly for early feedback) - **Dependency management** (build foundational pieces before dependent features) - **Testing and validation** (ensure each component works before building on it) ## Why Prioritize Implementation? ### 1. **Risk Mitigation** - Identify technical challenges early when they're easier to fix - Validate critical assumptions before investing heavily in dependent features - Avoid "integration hell" by testing connections between components early ### 2. **Faster Feedback Loops** - Get a working MVP quickly to validate the core concept - Allow for course corrections based on real usage patterns - Build confidence in the architecture through incremental success ### 3. **Resource Efficiency** - Focus development effort on the most impactful features first - Avoid building complex features that might be unnecessary - Enable parallel work streams once foundations are solid ## AutoDocs MCP Server: Recommended Implementation Priorities ### 🚨 **Priority 1: Core Validation (Days 1-3)** **Goal**: Prove the fundamental concept works **Tasks**: 1. **Basic Project Setup** - Initialize hatch project with dependencies - Configure ruff, mypy, pytest - Set up CI/CD pipeline 2. **Minimal Viable Parser** - Implement `PyProjectParser` with basic functionality - Parse simple pyproject.toml files - Extract main dependencies only (skip optional deps initially) 3. **Simple MCP Integration** - Create basic FastMCP server - Implement `scan_dependencies` tool only - Test with real pyproject.toml files **Success Criteria**: - Can scan dependencies from real Python projects - MCP tool responds correctly in Cursor integration - Basic error handling works **Why First**: This validates that the core concept (parsing dependencies via MCP) actually works and integrates properly with Cursor. ### 🔥 **Priority 2: Documentation Fetching (Days 4-7)** **Goal**: Prove documentation retrieval and formatting works **Tasks**: 1. **PyPI API Integration** - Implement `PyPIDocumentationFetcher` - Handle network errors and rate limiting - Parse PyPI JSON responses 2. **Version-Based Caching** - Implement `VersionResolver` and cache key generation - Basic `FileCacheManager` with version-specific storage - No time-based expiration 3. **Basic Documentation Tool** - Implement simple `get_package_docs` tool - Format documentation for AI consumption - Test with variety of packages (requests, pydantic, fastapi) **Success Criteria**: - Can fetch and cache documentation for major packages - Version-specific caching works correctly - Documentation format is useful for AI context **Why Second**: This proves the documentation retrieval pipeline works before adding complexity like dependency context. ### ⚡ **Priority 3: Graceful Degradation (Days 8-10)** **Goal**: Make the system robust for real-world usage **Tasks**: 1. **Enhanced Error Handling** - Implement graceful degradation in dependency parsing - Handle malformed pyproject.toml files - Collect and return partial results with warnings 2. **Network Resilience** - Robust error handling for PyPI API failures - Retry logic with exponential backoff - Proper timeout handling 3. **User-Friendly Error Messages** - Actionable error messages with suggestions - Clear indication of partial success vs total failure **Success Criteria**: - Works with imperfect project configurations - Users get helpful information even when some things fail - No crashes on malformed input **Why Third**: Real-world Python projects are messy. Making the system robust ensures it's actually usable before adding advanced features. ### 🎯 **Priority 4: Dependency Context (Days 11-14)** **Goal**: Add the rich context that makes this system powerful **Tasks**: 1. **Dependency Resolution** - Implement `DependencyResolver` with relevance scoring - Smart selection of most important dependencies - Token budget management 2. **Context-Aware Tools** - Implement `get_package_docs_with_context` tool - Concurrent dependency fetching - Rich context formatting 3. **Performance Optimization** - Concurrent API requests with semaphore control - Efficient context window management - Cache optimization for dependency fetches **Success Criteria**: - Provides rich context with primary package + key dependencies - Respects token limits and performance requirements - Intelligent dependency selection works well **Why Fourth**: This is the "secret sauce" that differentiates this tool, but it depends on all the previous components working reliably. ### 🧪 **Priority 5: Testing & Polish (Days 15-21)** **Goal**: Production readiness and comprehensive testing **Tasks**: 1. **Comprehensive Test Suite** - Unit tests for all core components - Integration tests with real PyPI API - Performance benchmarks 2. **Production Features** - Cache cleanup and management - Monitoring and logging improvements - Configuration validation 3. **Documentation & Examples** - Usage documentation - Cursor integration guide - Troubleshooting guide **Success Criteria**: - >90% test coverage - Meets all performance benchmarks from spec - Ready for real-world usage **Why Last**: Polish and comprehensive testing are important but shouldn't delay validation of core concepts. ## Implementation Strategy Benefits ### ✅ **Early Risk Detection** By building core parsing and documentation fetching first, you'll quickly discover if there are fundamental issues with: - PyPI API reliability and rate limits - Documentation quality and format variations - MCP protocol integration challenges - Cursor compatibility issues ### ✅ **Incremental Value** Even after Priority 2, you have a useful tool: - Developers can scan project dependencies - Basic documentation lookup works - Provides immediate value while you build advanced features ### ✅ **Flexible Architecture Validation** Building incrementally validates your architectural decisions: - Interface designs prove themselves through use - Performance characteristics become clear early - Integration patterns establish themselves naturally ### ✅ **Parallel Development Opportunities** Once Priority 2 is complete: - One developer can work on graceful degradation (Priority 3) - Another can start on dependency context (Priority 4) - Testing can begin in parallel with feature development ## Alternative: Feature-Complete Approach (Not Recommended) ❌ **Why not build everything at once?** - **Higher risk**: If dependency context is complex, you might discover fundamental issues late - **Longer feedback cycles**: Can't test real usage until everything is built - **Integration challenges**: Harder to debug when everything is new - **Wasted effort**: Might build complex features that aren't actually needed ## Recommended Development Workflow 1. **Build -> Test -> Deploy -> Get Feedback -> Iterate** 2. **Start each priority with failing tests** (TDD approach) 3. **Deploy to staging environment** after each priority 4. **Get real user feedback** as early as Priority 2 5. **Adjust subsequent priorities** based on learnings This approach maximizes learning, minimizes risk, and delivers value incrementally while building toward the complete vision. --- # Expansion Phase Priorities (Post-MVP) ## Current Status: MVP Complete ✅ **Achieved**: Priorities 1-4 complete with production-ready Python ecosystem support **Next**: Expansion into documentation sources and multi-language support ## 🚀 **Expansion Priority 1: Universal Documentation Sources (Weeks 1-4)** **Goal**: Provide rich documentation beyond PyPI for ALL current and future language ecosystems **Strategic Value**: - Immediately benefits existing Python users - Creates foundation for all future language ecosystems - Universal value proposition (GitHub works for every language) ### **Sub-Priority 1A: GitHub Integration (Weeks 1-2)** **Goal**: Add GitHub repository documentation to Python packages **Tasks**: 1. **Repository URL Extraction** - Parse PyPI `project_urls` for GitHub/GitLab links - Handle URL variations and redirects - Implement fallback discovery strategies 2. **GitHub API Integration** - Implement authenticated GitHub API client - README file fetching with format detection (Markdown, RST) - Examples directory scanning and content extraction - Rate limiting and error handling (5000 requests/hour) 3. **Content Processing & Integration** - Markdown/RST to AI-optimized text conversion - Code example extraction and highlighting - Integration with existing PyPI documentation formatting - Query-based content filtering **Success Criteria**: - 80%+ Python packages have discoverable GitHub repositories - README content successfully extracted and formatted - Measurable improvement in AI coding suggestion quality - Sub-5-second response times maintained **Risk Mitigation**: - GitHub rate limiting through intelligent caching and batching - Graceful degradation when repositories are unavailable - Content quality assessment to prioritize valuable documentation ### **Sub-Priority 1B: Read the Docs Integration (Week 3)** **Goal**: Add structured API documentation for Python packages **Tasks**: 1. **RTD Detection & Access** - Detect Read the Docs URLs from PyPI metadata - Handle custom documentation domains - Site availability validation 2. **Documentation Scraping** - HTML parsing for API reference sections - Tutorial and guide content extraction - Structured content organization 3. **Multi-Format Support** - Sphinx documentation parsing - MkDocs site structure handling - Generic HTML documentation fallback **Success Criteria**: - 60%+ major Python packages have RTD documentation extracted - Structured API documentation improves AI responses - Integration with existing documentation sources ### **Sub-Priority 1C: Multi-Source Aggregation (Week 4)** **Goal**: Intelligently combine documentation from multiple sources **Tasks**: 1. **Source Intelligence** - Content deduplication algorithms - Source prioritization based on query and package type - Token budget management across sources 2. **Concurrent Processing** - Parallel source fetching with failure isolation - Performance optimization for multi-source requests - Smart caching strategies **Success Criteria**: - Comprehensive documentation for 90%+ Python packages - Intelligent source selection within token limits - Maintained performance with multiple sources --- ## 🌐 **Expansion Priority 2: Multi-Language Foundation (Weeks 5-8)** **Goal**: Establish universal architecture and add highest-impact language ecosystem **Strategic Value**: - 10x+ market expansion (Python → Python + JavaScript) - Validates universal architecture design - Targets largest developer community (65% use JavaScript) ### **Sub-Priority 2A: Universal Architecture (Week 5)** **Goal**: Create language-agnostic foundation **Tasks**: 1. **Core Abstractions** - `LanguageEcosystem` interface design - `UniversalMCPServer` implementation - Language detection system 2. **Unified Caching Strategy** - Cross-language cache management - Language-prefixed cache keys - Shared documentation source integration **Success Criteria**: - Clean separation between language-specific and universal code - Extensible architecture validated with Python ecosystem - Performance maintained with universal layer ### **Sub-Priority 2B: Node.js Ecosystem (Weeks 6-8)** **Goal**: Complete second language ecosystem support **Tasks**: 1. **Dependency Parsing** - `package.json` parsing (dependencies, devDependencies, peerDependencies) - Lock file integration (`package-lock.json`, `yarn.lock`) - Scoped package handling (`@org/package`) - Monorepo and workspace support 2. **Documentation Sources** - npm Registry API integration - GitHub documentation (reuse from Priority 1A) - TypeScript definition file parsing - JSDoc extraction and formatting 3. **Testing & Integration** - Test with major Node.js packages (express, react, lodash) - Monorepo project validation - Performance benchmarking **Success Criteria**: - Complete Node.js dependency parsing and resolution - Rich documentation from npm + GitHub sources - Universal MCP tools work across Python and Node.js - 2x developer market coverage achieved --- ## 🎯 **Expansion Priority 3: Rapid Language Addition (Weeks 9-12)** **Goal**: Add simple ecosystems to validate scalability and increase coverage ### **Sub-Priority 3A: Go Language Support (Weeks 9-10)** **Goal**: Validate architecture with simple, well-designed ecosystem **Tasks**: - `go.mod` and `go.sum` parsing - pkg.go.dev API integration - Major version handling (v2, v3+ import path changes) - GitHub integration (reuse existing) **Strategic Value**: Growing ecosystem (15% developers), simple dependency model ### **Sub-Priority 3B: Performance & Polish (Weeks 11-12)** **Goal**: Optimize multi-language system for production use **Tasks**: - Multi-language performance optimization - Advanced caching strategies - Monitoring and observability - Documentation and user guides **Success Criteria**: - 3+ language ecosystems supported - Sub-5-second performance across all languages - Production-ready monitoring and error handling --- ## 🏢 **Expansion Priority 4: Enterprise Ecosystems (Weeks 13-20)** **Goal**: Support high-value enterprise development ecosystems ### **Sub-Priority 4A: .NET Ecosystem (Weeks 13-16)** - `.csproj` and NuGet parsing - NuGet API and Microsoft Docs integration - Enterprise repository support ### **Sub-Priority 4B: Java Ecosystem (Weeks 17-20)** - Maven and Gradle build system support - Maven Central API integration - Complex dependency resolution **Strategic Value**: Large enterprise markets, validates complex ecosystem handling --- ## Key Expansion Principles ### 1. **Universal Foundation First** Every expansion builds capability for ALL current and future languages - GitHub integration benefits Python immediately, enables all future languages - Universal architecture supports infinite language additions ### 2. **Market Impact Prioritization** Focus on maximum developer coverage: - Phase 1: Python (current) + JavaScript = ~80% of web developers - Phase 2: Add Go (growing, simple) = validate scaling - Phase 3: Add enterprise languages (.NET, Java) = enterprise market ### 3. **Technical Risk Management** - Simple ecosystems (Go) validate architecture before complex ones (Java) - Reuse proven patterns (GitHub integration) across languages - Graceful degradation ensures system remains useful during expansion ### 4. **Incremental Value Delivery** Each phase delivers immediate value: - Priority 1: Better Python documentation - Priority 2: Complete second language - Priority 3: Validates universal platform - Priority 4: Enterprise-ready solution This expansion strategy transforms AutoDocs from a Python tool to the universal documentation intelligence platform for AI-assisted development.