Provides IDE-like semantic code retrieval and editing tools for LLMs, enabling precise code understanding and manipulation in large codebases via the Model Context Protocol.
A Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.
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 agents to index local Python repositories and retrieve code intelligence through semantic search, caller lookups, hybrid context expansion, and visualization.
Provides semantic codebase understanding via a graph, enabling AI agents to search, explore, and plan changes with whole-repo context in a single tool call.
A structural codebase indexer that exposes 18 tools via the Model Context Protocol for AI-assisted code navigation, enabling efficient querying of functions, classes, dependencies, and call chains without reading entire files.