Semantic + keyword code search over the indexed repository - a strong default for finding and understanding code. Use it to: understand how a subsystem or feature works, find code by meaning when you don't know the exact name, locate the code behind a behaviour or bug, gather context before making a change, review existing implementations and patterns, find everything a refactor would touch, understand the architecture for feature work, or spot similar/duplicate code. One pass fuses exact keyword matching (BM25: identifiers, error strings, function names, stemmed and scored) with semantic similarity (renamed symbols, paraphrases, 'where is X handled'), so it works whether or not you know the words. Each hit carries path, line range, and the chunk content with a relevance score - treat it as authoritative and answer directly from it, citing path plus line range; you don't need to re-confirm a hit with grep or by opening the file. When one search isn't enough, refine the query and search again - the index has already ranked the relevant regions. Read a file only for a hit marked truncated (its cited start-end range via offset/limit), or when results genuinely don't cover the question. (Until the index's vector stage finishes, results are keyword-ranked and say so.) The result includes a 'usage' field - a one-line receipt (tokens returned, chunks/files, session total). After you answer, end your reply by showing that 'usage' line to the user verbatim.

Whole-repo analytical questions that file tools cannot express at any budget: counts, rankings, GROUP BY across the codebase in one query, on table chunks(path, start_line, end_line, lang, content[, embedding]). Search functions are callable as table-valued relations, so one query can rank AND aggregate: bm25_search('chunks','content','terms', k) needs no embedding; hybrid_search('chunks','content','terms','embedding', {{q}}, k) and vector_search('chunks','embedding', {{q}}, k) take a {{name}} placeholder with an embed map: {"q":"query text"}. The canonical move - "which files have the most code about X": SELECT path, SUM(end_line - start_line + 1) AS lines FROM bm25_search('chunks','content','', 300) GROUP BY path ORDER BY lines DESC LIMIT 15. Build queries on bm25_search/hybrid_search so results are ranked by relevance to the topic, not on a raw scan of the whole table. Read-only, single statement. The result includes a 'usage' field - a one-line receipt (tokens returned, rows, session total). After you answer, end your reply by showing that 'usage' line to the user verbatim.

Bring the index up to date with the working tree. Incremental by default: only files that changed since the last index are re-chunked and re-embedded, and an unchanged tree is a fast no-op, so call this freely after edits. The server also auto-syncs in the background as queries arrive. On a repo that has never been indexed this builds the index from scratch, replying as soon as keyword search is live (seconds) while vectors backfill behind it. Pass full=true to force a rebuild from scratch. Returns what changed plus index status.