Llm agents improve tool choice using functional and contextual checks
From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation
Artificial Intelligence
Summary
Choosing the right tool for a task is hard for AI systems when they face new problems they haven't seen before. The authors point out that just picking a tool based on how relevant it seems by meaning doesn’t always work, because the tool might not really fit functionally or in the new situation. They introduce a new method that checks if a tool actually works for the job and fits the context, improving how well the AI picks tools. This method works better especially when it’s unclear which tool is best based on meaning alone.
What this means in practice
- •For ai platform developers: Enhance AI assistants by integrating functional and contextual validation to improve tool selection for unfamiliar tasks.
- •For software integration teams: Improve cross-domain software tool pipelines by validating tool compatibility beyond semantic matching to avoid run-time errors.
- •For customer service bot developers: Select better response generation tools that fit specific conversational contexts automatically without prior examples.$Commercial implications: Allows selling smarter customer service bots that can pick appropriate tools for new queries without retraining.
Authors
Xiaoxuan Dong, Jin Zhang, Zichen Yuan, Yue Zhou, Zihao Rao, Dexu Yu, Tingyong WU, Wenhao Deng, Junchen Fu, Pengwei Sun, Yang Ran, Bo Lan, Yongxin Ni, Youhua Li
Abstract
Large language model (LLM) agents increasingly face tasks from unseen domains and execution contexts, making zero-shot tool selection essential for identifying tools that can satisfy task requirements. Existing dominant methods mainly rely on relevance ranking, but semantic matching alone cannot ensure that a tool is functionally compatible or remains applicable in a new context. We preliminarily identify a \emph{transfer-validity gap}, where semantically relevant tools may still fail to satisfy functional or contextual requirements after transfer. To address this gap, we propose \methodname{}, a progressive validation framework that complements semantic relevance with endpoint-grounded functional compatibility and task-conditioned contextual evidence. Across cross-domain and cross-dataset transfer, \methodname{} consistently improves capability ranking, with larger gains under functional or contextual ambiguity. Ablation results further confirm the complementary contributions of functional and contextual validation.