Turning a Textbook into an Architecture Consultant with MCP and Knowledge Graphs

An MCP server that extracts a knowledge graph from Agentic Architectural Patterns for Building Multi-Agent Systems (Arsanjani & Bustos, Packt 2026 [] ) — 138 concepts, 462 relationships, 786 sections — and uses it to run structured architecture consultations inside Claude Code. You point it at a codebase, it matches concepts via embeddings, spawns parallel subagents to traverse the graph for prerequisites/conflicts/alternatives, scores maturity against the book’s Chapter 12 rubric (7 categories,36 patterns), and renders an interactive HTML report with before/after architecture diagrams. I’ll walk through a pre-run consultation against OpenAI’s Financial Research Agent (https://github.com/openai/openai-agents-python/tree/main/examples/financial_research_agent), scrolling through the real terminal output to show each phase: concept matching, scatter-gather subagent traversal, pattern assessment logging, deterministic scoring, and server-side report rendering.