CodeGrok MCP: Semantic Code Search That Saves AI Agents 10x in Context Usage

AI coding agents using grep/ripgrep waste thousands of tokens and context on false positives. CodeGrok MCP uses AST-based semantic search with local vector embeddings to return only relevant code chunks. It parses code into symbols, generates 768-dim embeddings via CodeRankEmbed, and stores them in ChromaDB all locally. The result: semantic understanding instead of keyword matching.