Although　Large　Language　Models　(LLMs)　excel　in　NLP　tasks,　they　still　need　external　tools　to　extend　their　ability.　Current　research　on　tool　learning　with　LLMs　often　assumes　mandatory　tool　use,　which　does　not　always　align　with　real-world　situations,　where　the　necessity　for　tools　is　uncertain,　and　incorrect　or　unnecessary　use　of　tools　can　damage　the　general　abilities　of　LLMs.　Therefore,　we　propose　to　explore　whether　LLMs　can　discern　their　ability　boundaries　and　use　tools　flexibly.　We　then　introduce　the　Whether-or-not　tool　usage　Evaluation　benchmark　(WTU-Eval)　to　assess　LLMs　with　eleven　datasets,　where　six　of　them　are　tool-usage　datasets,　and　five　are　general　datasets.　LLMs　are　prompted　to　use　tools　according　to　their　needs.　The　results　of　eight　LLMs　on　WTU-Eval　reveal　that　LLMs　frequently　struggle　to　determine　tool　use　in　general　datasets,　and　LLMs＇　performance　in　tool-usage　datasets　improves　when　their　ability　is　similar　to　ChatGPT.　©　2025　IEEE.