Agent-Facing Information Design in LLM Tool Registries: A Preregistered Test of Rhetoric, Position and Structure

תקציר מקורי באנגליתarXiv:2605.23916v2 Announce Type: replace-cross Abstract: AI agents often pick tools from registries, where each tool's provider writes its description. We ask whether sales language in those descriptions changes which tool an agent picks. We built pairs of listings differing in one controlled way (added praise, a verifiable specification, or list order) and asked two OpenAI models to call one tool. In a preregistered study, stacked praise (four kinds combined) raised a tool's pick rate by about 43 percentage points, matching or beating a verifiable specification. Praise also pulled some picks toward tools that could not do the task, but rarely toward tools asking for unneeded data access. With identical listings, the first-listed tool was picked about 72 points more often. On tasks with n