mcp-adr-analysis-server

# ADR-002: AI Integration and Advanced Prompting Strategy ## Status Accepted ## Context The MCP ADR Analysis Server integrates advanced AI capabilities for architectural analysis, including Knowledge Generation, Reflexion learning, and Automatic Prompt Engineering (APE). The system needs to provide high-quality analysis with confidence scoring, evidence-based recommendations, and systematic verification processes. The choice of AI integration approach affects analysis quality, response time, system complexity, and reliability. ## Decision We will implement a hybrid AI integration approach using advanced prompting techniques (Knowledge Generation + Reflexion + APE) with external AI services, combined with local caching and methodological pragmatism framework for systematic verification and confidence scoring. Key components: - **Knowledge Generation**: Domain-specific architectural knowledge enhancement - **Reflexion Learning**: Learning from past analysis outcomes and experiences - **Automatic Prompt Engineering**: Optimized prompt generation for better results - **Confidence Scoring**: Systematic confidence assessment for all recommendations - **Evidence-Based Analysis**: All recommendations backed by concrete evidence - **Methodological Pragmatism**: Explicit fallibilism and systematic verification ## Consequences **Positive:** - Enhanced analysis quality with confidence scoring and evidence backing - Systematic verification processes reduce false positives - Learning from past experiences improves future analysis - Methodological pragmatism provides structured approach to uncertainty - Advanced prompting techniques improve AI response quality - Local caching reduces latency and external service dependency **Negative:** - Increased complexity in prompt management and AI workflow orchestration - Dependency on external AI services for advanced analysis - Potential latency in analysis due to multi-step AI processing - Need for sophisticated error handling and fallback mechanisms - Higher computational costs due to advanced prompting techniques - Complexity in managing confidence scoring and evidence validation ## Evolution Notes (2025) > **CE-MCP Paradigm Shift**: This ADR documents the original AI integration strategy with advanced prompting techniques. As of 2025, the CE-MCP paradigm shifts the LLM's role from step-by-step planner to holistic code generator. See **ADR-014** for the complete evolution. **Key Changes in CE-MCP:** - LLM generates complete orchestration scripts (Python/TypeScript) instead of sequential tool calls - Context assembly moves from upfront composition to sandbox-based lazy loading - Intermediate results stay in sandbox memory rather than passing through LLM context - Prompt loading becomes on-demand via catalog registry (96% token reduction) **This ADR Remains Valid For:** - Knowledge Generation concepts (moved to sandbox operations) - Reflexion Learning principles (state managed in sandbox) - Confidence Scoring methodology - Evidence-Based Analysis requirements - Methodological Pragmatism framework **Token Optimization Context:** Analysis revealed inefficiencies in current implementation: - 6,145 lines of prompts (~28K tokens) loaded upfront - 121+ AI call points with intermediate result embedding - Sequential context assembly (9K-12K tokens before LLM call) **Superseded By ADR-014 For:** - Prompt loading strategy (now lazy-loading registry) - Context composition patterns (now sandbox directives) - Multi-step AI workflow orchestration (now code-generated) ## Related ADRs - ADR-001: MCP Protocol Implementation Strategy (foundation) - ADR-014: CE-MCP Architecture (evolves this ADR)