"Answer" matching MCP servers:

Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.

Consolidates git history, test results, and Copilot sessions into a searchable SQLite database for AI assistants to answer questions about development activity.