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ad53673a28
The global-mode search_resource_types tool described its query as a substring match, but the backend does semantic embedding search. Rewrite the description so the LLM sends natural-language intent instead of literal name guesses. Apply the top-score trim to query_resource_types so a strong match isn't diluted by weakly-related types that merely clear the 0.75 similarity floor. The trim was previously inlined only in query_hub_scripts; extract it into a shared trim_to_top_score helper, use it from both search paths, and add a unit test pinning the 5% cutoff boundary. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>