Enables AI coding assistants to semantically search and retrieve relevant code patterns, documentation, and implementations from a codebase via MCP tools.
Enables AI agents to access a codebase context, select relevant files, and route queries to the appropriate AI model based on complexity, all through an MCP interface.
Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.
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.