nanobanana-mcp-server

# Nano Banana MCP Server - Implementation Plan ## Overview This document outlines a phased implementation plan for building the production-ready Nano Banana MCP Server, progressing from the existing skeleton to a fully-featured, scalable solution. ## Current State Assessment ### Existing Assets ✅ - Complete FastMCP server skeleton (`docs/fastmcp-skeleton.md`) - API design specification (`docs/mcp-server-design-01.md`) - System architecture design (`SYSTEM_DESIGN.md`) - API specification (`API_SPECIFICATION.md`) - Component design (`COMPONENT_DESIGN.md`) - Development guidance (`CLAUDE.md`) ### Missing Components 🔄 - Modular code structure - Comprehensive error handling - Input validation - Logging and monitoring - Testing framework - Production deployment configuration - Performance optimizations ## Implementation Phases ## Phase 1: Foundation & Refactoring (Week 1) ### Objectives - Refactor monolithic skeleton into modular components - Implement basic error handling and validation - Set up development environment ### Tasks #### 1.1 Project Structure Setup ```bash # Initialize project with uv uv init --python 3.11 # Add core dependencies uv add fastmcp google-genai pillow python-dotenv # Add development dependencies uv add --dev ruff pytest pytest-asyncio pytest-cov mypy # Create directory structure mkdir -p config core services tools resources prompts utils tests touch {config,core,services,tools,resources,prompts,utils,tests}/__init__.py ``` **Deliverables:** - [ ] Complete directory structure following `COMPONENT_DESIGN.md` - [ ] Base `__init__.py` files - [ ] `pyproject.toml` configured with uv dependencies - [ ] `ruff.toml` configuration for linting and formatting #### 1.2 Configuration Management **Files:** `config/settings.py`, `config/constants.py` ```python # Priority order 1. Environment variables 2. .env file 3. Default values ``` **Deliverables:** - [ ] `ServerConfig` class with environment variable loading - [ ] `GeminiConfig` class with API-specific settings - [ ] Configuration validation - [ ] `.env.example` file #### 1.3 Core Server Refactoring **Files:** `core/server.py`, `core/exceptions.py` **Tasks:** - [ ] Extract `NanoBananaMCP` class from skeleton - [ ] Implement component registration system - [ ] Add custom exception classes - [ ] Basic logging setup #### 1.4 Service Layer Implementation **Files:** `services/gemini_client.py`, `services/image_service.py` **Tasks:** - [ ] Create `GeminiClient` wrapper with error handling - [ ] Implement `ImageService` with generation and editing - [ ] Add input validation utilities - [ ] Basic retry logic for API calls **Acceptance Criteria:** - Server starts successfully with modular structure - Configuration loads from environment - Basic image generation works - Errors are properly handled and logged ## Phase 2: Core Functionality (Week 2) ### Objectives - Implement all tools, resources, and prompts - Add comprehensive input validation - Implement structured logging ### Tasks #### 2.1 Tool Implementation **Files:** `tools/generate_image.py`, `tools/edit_image.py`, `tools/upload_file.py` **Tasks:** - [ ] Migrate and enhance `generate_image` tool - [ ] Migrate and enhance `edit_image` tool - [ ] Migrate and enhance `upload_file` tool - [ ] Add parameter validation with Pydantic - [ ] Implement proper error responses #### 2.2 Resource Implementation **Files:** `resources/file_metadata.py`, `resources/template_catalog.py` **Tasks:** - [ ] Implement file metadata resource - [ ] Create enhanced template catalog with categories - [ ] Add resource caching mechanism - [ ] Error handling for missing resources #### 2.3 Prompt Implementation **Files:** `prompts/photography.py`, `prompts/design.py`, `prompts/editing.py` **Tasks:** - [ ] Organize prompts by category - [ ] Implement all 6 prompt templates - [ ] Add parameter validation - [ ] Create prompt testing utilities #### 2.4 Utility Enhancements **Files:** `utils/image_utils.py`, `utils/validation_utils.py`, `utils/logging_utils.py` **Tasks:** - [ ] Image format validation and conversion - [ ] Size optimization utilities - [ ] Structured logging configuration - [ ] Input sanitization utilities **Acceptance Criteria:** - All tools, resources, and prompts functional - Comprehensive parameter validation - Structured logging with proper levels - Error messages are user-friendly ## Phase 3: Production Features (Week 3) ### Objectives - Add production-ready features - Implement monitoring and health checks - Performance optimizations ### Tasks #### 3.1 Advanced Error Handling **Files:** `core/middleware.py`, `core/exceptions.py` **Tasks:** - [ ] Global exception handler middleware - [ ] Circuit breaker for external API calls - [ ] Retry logic with exponential backoff - [ ] Rate limiting implementation - [ ] Request timeout handling #### 3.2 Health & Monitoring **Files:** `core/health.py`, `utils/metrics.py` **Tasks:** - [ ] Health check endpoints (HTTP mode) - [ ] Metrics collection (request counts, latency) - [ ] Performance monitoring - [ ] Resource usage tracking - [ ] API quota monitoring #### 3.3 Security Enhancements **Files:** `core/security.py`, `utils/validation_utils.py` **Tasks:** - [ ] API key validation and rotation support - [ ] Input sanitization for all endpoints - [ ] Request size limits - [ ] CORS configuration for HTTP mode - [ ] Security headers implementation #### 3.4 Performance Optimizations **Files:** `services/cache_service.py`, `utils/performance.py` **Tasks:** - [ ] Template caching system - [ ] Response compression - [ ] Connection pooling for API calls - [ ] Memory usage optimization - [ ] Async processing where possible **Acceptance Criteria:** - Server handles high load gracefully - Health checks provide detailed status - Security best practices implemented - Performance metrics are collected ## Phase 4: Testing & Quality Assurance (Week 4) ### Objectives - Comprehensive test coverage - Integration testing - Performance testing ### Tasks #### 4.1 Unit Testing **Files:** `tests/test_*.py` **Test Categories:** - [ ] Configuration loading (`test_config.py`) - [ ] Tool functionality (`test_tools.py`) - [ ] Service layer (`test_services.py`) - [ ] Resource access (`test_resources.py`) - [ ] Prompt generation (`test_prompts.py`) - [ ] Utility functions (`test_utils.py`) **Coverage Target:** 90%+ #### 4.2 Integration Testing **Files:** `tests/integration/test_*.py` **Test Categories:** - [ ] End-to-end tool execution - [ ] Gemini API integration - [ ] File upload/download cycles - [ ] Error handling scenarios - [ ] Multi-step workflows #### 4.3 Performance Testing **Files:** `tests/performance/test_*.py` **Test Categories:** - [ ] Concurrent request handling - [ ] Memory usage profiling - [ ] Large file processing - [ ] API rate limit testing - [ ] Stress testing #### 4.4 Quality Assurance **Tools:** `pytest`, `coverage`, `mypy`, `black`, `flake8` **Tasks:** - [ ] Set up CI/CD pipeline - [ ] Code quality checks - [ ] Type checking - [ ] Documentation generation - [ ] Security scanning **Acceptance Criteria:** - 90%+ test coverage - All integration tests pass - Performance benchmarks met - Code quality standards satisfied ## Phase 5: Deployment & Documentation (Week 5) ### Objectives - FastMCP native deployment setup - Comprehensive documentation - User guides and examples ### Tasks #### 5.1 FastMCP Deployment Setup **Files:** `systemd/`, `scripts/` **Tasks:** - [ ] FastMCP native deployment configuration - [ ] Systemd service files for production - [ ] Process management scripts - [ ] Environment configuration templates - [ ] Health monitoring setup #### 5.2 Documentation **Files:** `docs/` **Documentation Types:** - [ ] API documentation (OpenAPI spec) - [ ] User guide with examples - [ ] Developer setup guide - [ ] Deployment guide - [ ] Troubleshooting guide - [ ] Performance tuning guide #### 5.3 Examples & Tutorials **Files:** `examples/` **Examples:** - [ ] Basic image generation - [ ] Advanced prompt engineering - [ ] FastMCP deployment examples - [ ] Integration with popular tools - [ ] Error handling patterns **Acceptance Criteria:** - Server deploys successfully with FastMCP native deployment - Documentation is comprehensive and accurate - Examples work as documented - Production readiness checklist complete ## Development Guidelines ### Code Quality Standards #### Style & Formatting (Using Ruff) ```bash # Code formatting and linting with ruff uv run ruff format src/ tests/ uv run ruff check src/ tests/ # Type checking uv run mypy src/ # Testing uv run pytest tests/ --cov=src/ --cov-report=html ``` #### Commit Standards ``` feat: add image generation caching fix: handle API timeout errors docs: update deployment guide test: add integration tests for file upload refactor: extract validation utilities ``` ### Development Workflow #### 1. Feature Development ```bash git checkout -b feature/image-caching # Install dependencies uv sync # Develop feature with quality checks uv run ruff format . uv run ruff check . uv run mypy src/ # Write tests uv run pytest tests/ --cov=src/ # Update documentation git commit -m "feat: implement image result caching" ``` #### 2. Code Review Checklist - [ ] Functionality works as specified - [ ] Tests written and passing - [ ] Ruff formatting and linting pass - [ ] Type checking with mypy passes - [ ] Error handling implemented - [ ] Documentation updated - [ ] Performance impact assessed - [ ] Security considerations reviewed #### 3. Integration Process ```bash # Run full test suite uv run pytest tests/ --cov=src/ --cov-report=html # Check code quality with ruff uv run ruff check . uv run ruff format --check . uv run mypy src/ # Run server integration test uv run python server.py & # Basic smoke test curl -X POST http://localhost:8000/health kill %1 # Merge to main git checkout main && git merge feature/image-caching ``` ## Resource Planning ### Team Requirements - **Backend Developer**: Python/FastAPI expertise - **DevOps Engineer**: Docker/Kubernetes deployment - **QA Engineer**: Testing automation - **Technical Writer**: Documentation ### Infrastructure Requirements - **Development**: Local Docker environment - **Testing**: CI/CD pipeline (GitHub Actions) - **Production**: Container orchestration platform - **Monitoring**: Logging and metrics infrastructure ### Timeline Summary | Phase | Duration | Key Deliverables | |-------|----------|------------------| | Phase 1 | Week 1 | Modular structure, basic functionality | | Phase 2 | Week 2 | Complete feature set, validation | | Phase 3 | Week 3 | Production features, monitoring | | Phase 4 | Week 4 | Testing, quality assurance | | Phase 5 | Week 5 | Deployment, documentation | **Total Duration:** 5 weeks **Total Effort:** ~200 hours ## Success Metrics ### Functionality Metrics - ✅ All tools, resources, and prompts working - ✅ 100% API specification compliance - ✅ Error handling for all edge cases ### Quality Metrics - ✅ 90%+ test coverage - ✅ Zero security vulnerabilities - ✅ All code quality checks passing ### Performance Metrics - ✅ <5s average image generation time - ✅ Support for 20+ concurrent users - ✅ <100MB memory usage per instance ### Production Readiness - ✅ Automated deployment pipeline - ✅ Comprehensive monitoring - ✅ Complete documentation ## Risk Mitigation ### Technical Risks 1. **Gemini API Changes**: Monitor API documentation, implement version detection 2. **Performance Issues**: Early performance testing, profiling tools 3. **Security Vulnerabilities**: Regular security scanning, dependency updates ### Schedule Risks 1. **Scope Creep**: Strict phase definitions, change control 2. **Dependencies**: Parallel development where possible 3. **Integration Issues**: Early integration testing ### Quality Risks 1. **Test Coverage**: Automated coverage reporting, quality gates 2. **Documentation Gaps**: Documentation reviews, user feedback 3. **Production Issues**: Staged deployment, rollback procedures This implementation plan provides a structured approach to building the Nano Banana MCP Server from the existing skeleton to a production-ready system, with clear milestones, deliverables, and success criteria.