Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.
Provides intelligent code indexing with 15 MCP tools for symbol extraction, hybrid search (FTS5+vector), call graphs, and incremental indexing of local folders and remote repos, enabling token-efficient code retrieval for AI agents.
AST-aware codebase indexing with semantic search, exposed as an MCP server. Enables semantic search and file context retrieval across your codebase using natural language queries.
Provides MCP tools for Claude Code to query a project's codebase via a compact index, reducing token usage and context bloat by enabling symbol lookup, usage tracking, and dead-code detection.