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The author shares their experience switching from semantic embeddings to BM25 for tool selection in agents, finding that BM25 achieves 81% top-1 accuracy vs. 64% for embeddings on a corpus of 200 query-tool pairs, because tool descriptions are short and keyword-driven rather than semantically rich like documents.
A Japanese developer demonstrates how using Claude Code's "find skills" feature enables optimal tool selection for coding tasks.