AutoDocs MCP Server

# Cross-Project Technology Decisions **Last Updated**: August 11, 2025 **Technology Lead**: Development Team **Review Frequency**: Quarterly ## 🎯 Technology Strategy Overview ### Strategic Principles - **Consistency**: Minimize technology fragmentation across projects - **Proven Technologies**: Favor mature, well-supported tools over bleeding edge - **Team Expertise**: Align technology choices with team skills and experience - **Ecosystem Synergy**: Choose technologies that work well together - **Future-Proofing**: Select technologies with strong long-term prospects ### Technology Portfolio Status - **Python Ecosystem**: Mature, stable foundation for backend development - **Web Technologies**: Modern JavaScript/TypeScript stack for frontend - **AI/ML Integration**: Focus on OpenAI API and MCP protocol - **Development Tools**: Comprehensive toolchain for quality and productivity --- ## 🐍 Python Technology Stack ### Core Language & Runtime - **Python Version**: 3.12+ (latest stable) - **Package Manager**: `uv` (fast, modern Python package management) - **Virtual Environment**: `uv venv` for isolated development environments - **Dependency Resolution**: `uv` with `pyproject.toml` configuration **Rationale**: Python 3.12 provides excellent performance and modern features. `uv` offers significant speed improvements over pip/pipenv while maintaining compatibility. ### Web Framework & API Development - **MCP Server Framework**: `FastMCP` - Specialized for MCP protocol implementation - **Alternative Web Framework**: `FastAPI` - If non-MCP web APIs needed - **Async Runtime**: Built-in `asyncio` with async/await patterns - **HTTP Client**: `httpx` - Modern async HTTP client with excellent features **Rationale**: FastMCP provides MCP-specific optimizations. FastAPI offers excellent async support, automatic documentation, and type safety. httpx is the modern standard for async HTTP. ### Data & Validation - **Data Models**: `Pydantic v2` - Type-safe data validation and serialization - **Configuration**: `Pydantic Settings` - Environment-aware configuration management - **JSON Processing**: Standard library `json` with Pydantic serialization - **File Formats**: `toml` library for pyproject.toml parsing **Rationale**: Pydantic v2 provides excellent performance, type safety, and validation capabilities. Native integration with FastAPI and modern Python practices. ### Database & Caching (When Needed) - **File-based Caching**: JSON files with atomic writes (current AutoDocs approach) - **Future Database**: `SQLite` with `SQLModel` for simple relational needs - **Advanced Database**: `PostgreSQL` with `asyncpg` for complex requirements - **Redis**: For distributed caching if multi-instance deployment needed **Rationale**: Start simple with file-based caching, scale up to SQLite, then PostgreSQL as needed. SQLModel provides Pydantic integration with SQL databases. --- ## 🌐 Web Development Stack ### Frontend Framework - **Primary Choice**: `Next.js` (React-based) for full-stack applications - **Alternative**: `Vite + React` for SPA applications - **Styling**: `Tailwind CSS` for utility-first styling - **Component Library**: `Shadcn/ui` or `Chakra UI` for consistent components **Rationale**: Next.js provides excellent developer experience, SSR/SSG capabilities, and strong ecosystem. Tailwind CSS offers rapid development with consistent design. ### TypeScript & Tooling - **Language**: `TypeScript` for type safety and better developer experience - **Build Tool**: `Vite` (standalone) or Next.js build system - **Package Manager**: `npm` or `yarn` with lock files - **Bundling**: Built-in bundling with framework tools **Rationale**: TypeScript prevents many runtime errors and improves code maintainability. Modern build tools provide excellent developer experience. ### State Management & API - **State Management**: React Context API for simple state, `Zustand` for complex - **API Calls**: `fetch` API with TypeScript wrappers - **Form Handling**: `React Hook Form` with validation - **Routing**: Framework-provided routing (Next.js Router, React Router) **Rationale**: Start simple with Context API, scale to Zustand as needed. Native fetch API is now sufficient for most HTTP needs. --- ## 🤖 AI & ML Integration ### AI Model Integration - **Primary AI Provider**: OpenAI API (Claude via Anthropic API) - **MCP Protocol**: Core protocol for AI tool integration - **Model Context Protocol**: `FastMCP` library for server implementation - **AI Development**: Claude Code as primary development environment **Rationale**: Focus on MCP protocol for maximum compatibility with AI development environments. OpenAI/Anthropic APIs provide reliable, high-quality AI capabilities. ### AI-Specific Libraries - **OpenAI Client**: `openai` Python library - **Anthropic Client**: `anthropic` Python library - **Text Processing**: Standard library `re`, `json` for simple needs - **Advanced NLP**: Consider `spaCy` or `transformers` if needed **Rationale**: Official client libraries provide best support and reliability. Avoid complex NLP libraries unless specifically needed. --- ## 🛠️ Development & Quality Tools ### Code Quality & Formatting - **Formatter**: `Ruff` (replaces Black, isort, Flake8, and many plugins) - **Type Checker**: `MyPy` with strict configuration - **Pre-commit**: `pre-commit` hooks for automated quality checks - **Git Hooks**: Enforce quality standards before commits **Rationale**: Ruff consolidates many tools into one fast, reliable solution. MyPy provides excellent type checking with gradual adoption. ### Testing Framework - **Test Runner**: `pytest` - Industry standard with excellent plugin ecosystem - **Async Testing**: `pytest-asyncio` for async/await test support - **Mocking**: `pytest-mock` (never use `unittest.mock` directly) - **HTTP Testing**: `pytest-httpx` for HTTP client mocking - **Coverage**: `pytest-cov` with coverage reporting - **Parallel Testing**: `pytest-xdist` for faster test execution **Rationale**: pytest ecosystem is mature, comprehensive, and provides excellent developer experience. Plugin system allows customization without complexity. ### Documentation & API Tools - **API Documentation**: `OpenAPI` (Swagger) for REST APIs - **MCP Documentation**: JSON Schema for MCP tool definitions - **Code Documentation**: Python docstrings with Google style - **Project Documentation**: Markdown files with consistent structure **Rationale**: Industry standard documentation formats ensure compatibility and tooling support. --- ## 🚀 Deployment & Operations ### Containerization & Deployment - **Current Deployment**: Direct Python package installation (`pip install -e .`) - **Future Containerization**: `Docker` with multi-stage builds if needed - **Process Management**: `systemd` for Linux deployments - **Package Distribution**: `PyPI` for Python packages **Rationale**: Start simple with direct installation, containerize when deployment complexity requires it. ### Monitoring & Observability - **Logging**: Python `structlog` for structured logging - **Metrics**: Custom metrics collection with JSON format - **Health Checks**: HTTP endpoints for health and readiness - **Error Tracking**: Built-in error handling with structured logging **Rationale**: Structured logging provides better observability. Simple metrics collection avoids vendor lock-in. ### CI/CD Pipeline - **Version Control**: `Git` with GitHub - **CI/CD Platform**: `GitHub Actions` for automation - **Automated Testing**: Full test suite execution on every PR - **Quality Gates**: Pre-commit hooks + CI validation - **Deployment**: Automated PyPI publication for Python packages **Rationale**: GitHub Actions integrates well with GitHub repositories and provides sufficient capabilities for current needs. --- ## 📊 Technology Decision Matrix ### Evaluation Criteria When evaluating new technologies, use this framework: | Criterion | Weight | Description | |-----------|--------|-------------| | **Team Expertise** | 25% | How well does team know this technology? | | **Ecosystem Maturity** | 20% | How mature and stable is the ecosystem? | | **Maintenance Burden** | 20% | How much ongoing maintenance will this require? | | **Performance** | 15% | Does this meet our performance requirements? | | **Community Support** | 10% | Quality of documentation, community, support | | **Future Viability** | 10% | Will this technology be supported long-term? | ### Decision Process 1. **Identify Need**: Clearly define the problem to be solved 2. **Research Options**: Identify 2-3 viable alternatives 3. **Evaluate**: Score each option against criteria 4. **Prototype**: Build small proof-of-concept if needed 5. **Decide**: Choose option with highest weighted score 6. **Document**: Record decision rationale and alternatives considered --- ## 🔄 Technology Evolution & Maintenance ### Regular Technology Reviews - **Quarterly Assessment**: Review technology choices for continued relevance - **Annual Deep Dive**: Comprehensive technology stack evaluation - **Continuous Monitoring**: Track security vulnerabilities and updates - **Performance Monitoring**: Ensure technology choices meet performance goals ### Upgrade Strategy - **Security Updates**: Immediate application of security patches - **Minor Updates**: Regular updates during maintenance windows - **Major Updates**: Planned upgrades with testing and migration planning - **Technology Replacement**: Gradual migration when technology becomes obsolete ### Dependency Management - **Dependency Pinning**: Lock specific versions for reproducible builds - **Regular Updates**: Scheduled dependency update cycles - **Vulnerability Scanning**: Automated security vulnerability detection - **License Compliance**: Ensure all dependencies have compatible licenses --- ## 🚨 Technology Risk Management ### Risk Categories 1. **Technology Obsolescence**: Risk of chosen technology becoming deprecated 2. **Performance Issues**: Risk of technology not meeting performance needs 3. **Security Vulnerabilities**: Risk of security issues in dependencies 4. **Maintenance Burden**: Risk of high ongoing maintenance costs 5. **Team Expertise**: Risk of team lacking sufficient knowledge ### Risk Mitigation Strategies - **Diversification**: Avoid over-reliance on single vendors or technologies - **Standard Alternatives**: Choose widely-adopted technologies with alternatives - **Regular Assessment**: Proactive evaluation of technology health - **Team Training**: Continuous learning to maintain expertise - **Migration Planning**: Prepare migration paths for critical technologies --- ## 🎯 Future Technology Roadmap ### Short-term (6 months) - **Ruff Adoption**: Complete migration from multiple tools to Ruff - **uv Integration**: Standardize on uv for all Python project management - **Testing Enhancement**: Expand pytest plugin usage for better testing - **Documentation Tools**: Improve API documentation generation ### Medium-term (12-18 months) - **Containerization**: Evaluate Docker for deployment if complexity increases - **Advanced Monitoring**: Consider APM tools if observability needs grow - **Database Integration**: Add SQLite/SQLModel if data persistence needs arise - **Performance Optimization**: Profile and optimize technology stack performance ### Long-term (18+ months) - **Microservices**: Evaluate service decomposition if system grows complex - **Advanced AI**: Explore emerging AI technologies and integration patterns - **Scalability**: Plan for horizontal scaling if usage grows significantly - **New Paradigms**: Assess new programming paradigms and frameworks --- ## 📋 Technology Standards Enforcement ### Automated Enforcement - **Pre-commit Hooks**: Prevent commits that don't meet standards - **CI/CD Gates**: Block deployments that fail quality checks - **Dependency Scanning**: Automated security and license compliance - **Performance Testing**: Automated performance regression detection ### Manual Review Process - **Architecture Reviews**: Human review of significant technology decisions - **Code Reviews**: Ensure technology usage follows established patterns - **Regular Audits**: Periodic compliance assessment across projects - **Team Training**: Ensure team understands and follows technology standards --- *Technology decisions updated: August 11, 2025* *Next review: November 11, 2025* *Technology stack version: 1.0*