概要
AI coding agents often struggle with context overload, leading to wasted tokens and missed critical patterns due to irrelevant code. CodeWeaver solves this by functioning as an extensible context platform and MCP server that distills and delivers targeted, token-efficient context. It simplifies agent interaction with a single natural language `find_code` tool, eliminating confusion over multiple search methods. By utilizing agent-driven curation and a hybrid search pipeline combining text, semantic embeddings, and AST analysis, CodeWeaver significantly reduces context bloat by 60-80% while enhancing search precision, ensuring agents receive only the most relevant code with exact line and column references.