Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.
Enables AI coding agents to retrieve token-budgeted project context, search code symbols, look up definitions, and access project memory and cross-project learnings through local MCP tools. It reduces redundant exploration by supplying the smallest useful context from a deterministic, local-first index.
Enables AI agents to search code by meaning, explore codebase structure, store and query knowledge with temporal facts, and read source code through a set of MCP tools.
Provides AI agents with a live architecture model of a codebase, enabling queries for root cause analysis, blast radius, and dependency traversal through MCP tools.