An MCP server that provides ultra-efficient code exploration through AST analysis, reducing LLM token usage by up to 95% while enabling instant call graph generation and dependency analysis for massive codebases.
MCP server that analyzes codebases to provide dependency graphs, impact analysis, and file insights across 15+ programming languages, enabling AI assistants to understand project structure and navigate code efficiently.
An MCP server that transforms codebases into intelligent, queryable knowledge bases, enabling AI assistants to perform semantic search, explore architecture, and analyze code relationships.
This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.
An MCP server that transforms codebases into knowledge graphs using Neo4J, enabling AI assistants to understand code structure, relationships, and metrics for more context-aware assistance.
MCP server that exposes Python function- and module-level call graphs for agentic coding clients, enabling tools like callers_of, callees_of, and neighborhood queries.
