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 query a local, continuously updated symbol graph of a codebase, providing ranked search, caller/callee exploration, dependency paths, and git-diff impact analysis through MCP tools.
Provides code intelligence for AI coding agents by indexing repositories into a hybrid knowledge graph, enabling agents to query dependencies, impact, and context through 28 MCP tools.
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 assistants with a structured, token-efficient map of a codebase's symbols, dependencies, and relationships via MCP tools like overview, query, and impact analysis.
Enables AI coding agents to query a semantic code graph over MCP, retrieving exact function source, definitions, callers/callees, impact analysis, and minimal test selection instead of whole files.