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- 01 AI agents' built-in grep tool issues cause cascading failures: false negatives, token saturation, hallucination
- 02 Tree-sitter AST chunking preserves semantic integrity at function/class level, supporting 18+ languages
- 03 Hybrid search (vector + FTS + RRF) delivers up to 30% accuracy improvement, up to 62% on chunked long documents
- 04 Runs entirely locally: no API keys, no cloud dependency, local embedding via sentence-transformers
+ What is an MCP server and why is it needed?
MCP (Model Context Protocol) is a protocol that enables AI agents to communicate with external tools. This MCP server enables agents to perform semantic search across a codebase. While built-in grep tools have a false negative rate reaching 50%, hybrid search improves accuracy by up to 30%.
+ Why was LanceDB chosen?
LanceDB is serverless and file-based, requiring no separate database server. Its FTS (full-text search) support enables hybrid search. Alternatives like PostgreSQL+pgvector or Qdrant require additional infrastructure.
+ What does Tree-sitter AST chunking mean?
Tree-sitter is a parser that converts source code into an abstract syntax tree (AST). AST-aware chunking splits code by functions and classes rather than by lines. This ensures each chunk forms a semantically meaningful unit, improving search accuracy.
+ Which AI agents does this server work with?
It works with any agent that supports the MCP protocol. Starting with Claude Code, other coding agents with MCP support can also use this server. Installation takes a single command.