Agentic Quality Management and Analytics MCP System

Built an MCP (Model Context Protocol) server that brings a large-scale automated visual/typography QA testing pipeline directly into an AI coding assistant. Thirteen MCP tools span two tiers: instant, pre-computed metrics pulled from a CDN (test counts, pass/fail, quality classification, run metadata) in about 100ms, and a deep-analysis mode that loads a full run locally and runs SQL-style queries — error-code distribution, document-type breakdown, custom field filters — via DuckDB in well under a second once loaded.

The roadmap layers on a 5-source hybrid search engine (BM25 + vector search via ChromaDB and Sentence Transformers, image-level analysis via BLIP-2, unified through LiteLLM), a four-stage analytics maturity model (descriptive → diagnostic → predictive → prescriptive), and D3-based report generation.

Engineering standard enforced on every change: 100% test pass rate, 80%+ coverage, no mocks — tests run against real implementations.