A MCP server that transforms code repositories from GitHub, GitLab, or local directories into LLM-friendly formats, preserving context and structure for better AI processing.
An MCP server that analyzes codebases and generates contextual prompts, making it easier for AI assistants to understand and work with code repositories.
An MCP server that scans codebases to extract structural information (classes, functions, etc.) with flexible filtering options and outputs in LLM-friendly formats.
A Model Context Protocol server that extracts and analyzes Python code structures, focusing on import/export relationships between files to help LLMs understand code context.
An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.