A study reveals that large language models (LLMs) often struggle with effectively deciding when to use external tools, highlighting a significant gap between their understanding and execution. This finding suggests that enhancing LLMs' tool-use reliability requires improvements in both recognizing tool necessity and translating that recognition into action.