Enables Claude Code and AI coding agents to retrieve compact, task-shaped code evidence bundles via MCP, combining AST-aware retrieval, hybrid BM25+FAISS search, call-graph traversal, reranking, and token-budgeted context assembly.
Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.
An MCP server that gives Claude structural understanding of a codebase—dependency graph, entry points, complexity ranking, and import cycles—without reading every file into context.
Extract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathers and makes concepts, naming conventions, and vocabulary queryable via MCP.
Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.
