Search
Tome indexes every enabled skill and command so you can find the right one by meaning, not just by name.
tome query
tome query "verify a Compact contract"
Search runs in two stages:
- KNN retrieval — your query is embedded with a local model and matched against the vector index to pull back the nearest candidates.
- Reranking — a local cross-encoder reranker re-scores those candidates so the most relevant results rise to the top.
Both models run on your machine; nothing is sent anywhere.
Scoping and flags
| Flag | Effect |
|---|---|
--catalog <name> | Restrict the search to a single catalog. |
--plugin <name> | Restrict the search to a single plugin. |
--strict | Fail (non-zero exit) instead of returning weak results when nothing meets the bar. |
--json | Emit machine-readable output. |
With --strict, a search that finds nothing relevant exits non-zero rather than
returning low-confidence matches — useful in scripts.
Why search matters: load on demand
The point of search is load on demand. Instead of stuffing every skill into your agent's context up front, the agent searches at runtime and loads only what the current task needs. That:
- protects the context window — skills that aren't relevant never take up space;
- cuts token spend — you pay for the skills you use, not your whole library;
- scales — a large catalog stays usable because retrieval, not context size, does the filtering.
Inside a configured harness, this same search runs over the
MCP server, so your agent gets search and skill loading
without you running tome query by hand.