  Recent advancements in integrating large language models (LLMs) with tools
have allowed the models to interact with real-world environments. However,
these tool-augmented LLMs often encounter incomplete scenarios when users
provide partial information or the necessary tools are unavailable. Recognizing
and managing such scenarios is crucial for LLMs to ensure their reliability,
but this exploration remains understudied. This study examines whether LLMs can
identify incomplete conditions and appropriately determine when to refrain from
using tools. To this end, we address a dataset by manipulating instances from
two datasets by removing necessary tools or essential information for tool
invocation. We confirm that most LLMs are challenged to identify the additional
information required to utilize specific tools and the absence of appropriate
tools. Our research can contribute to advancing reliable LLMs by addressing
scenarios that commonly arise during interactions between humans and LLMs.
