AI agents often find tools by searching their documentation. Agents that share a tool catalog can also pool usage examples: past requests, each tagged with the tool that handled it. We ask what pooling does to tools no agent has used yet. Adding each usage example to the search index as an extra entry for its tool (folding) helps tools that have examples, but on two of three catalogs it sharply lowers how often a needed unused tool reaches the top five results; on StableToolBench with our main embedding model, the share falls from 73 to 40 percent of queries. When the extra entries hold documentation from other tools instead of requests, the loss is much smaller, though differences in content or writing style could also explain this. We introduce ZoE (Z-scores over Evidence), a scoring rule that scores the documentation and the usage of a tool separately, so a tool without usage keeps its documentation score. On all three catalogs and with both embedding models we tested, ZoE retrieves unused tools more often than a router that reserves result slots for usage matches. With our main embedding model, the router retrieves more of the other tools on two catalogs, and in post hoc comparisons a simpler rule that adds a constant to the scores of tools with usage shows no detected difference from ZoE on unused tools. For five simulated agents on one catalog, folding lowered retrieval of unused tools for every agent; with ZoE no average drop was detected, though some agents still lost. Evaluations of pooled agent experience should report tools no agent has used separately from the average.

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