Large language models (LLMs) are becoming the centerpiece in the design and deployment of Agentic artificial intelligence (AI) systems. AI agents typically have (a) reasoning ability to analyze and think through the given task, (b) context/memory to remember things in the short-term and long-term, and (c) tools at their disposal to interact with the outsideworld. While solving the given task, it must decide whether tool use is required; if so, it must then select the appropriate tool and invoke it with the correct parameters. Although LLMs have advanced considerably in recent years, their tool-use capabilities remain limited. Even OpenAI’s most capable model to date, GPT-5, continues to struggle with reliable tool usage. In this paper, we propose TacTool, which empowers AI agents with improved tool selection and tool call formulation using different LLMs. We conduct experiments using Nestful and Berkeley Function Calling Leaderboard version 3 (BFCLv3) benchmarks and show that TacTool achieves ?27% and ?3% improvement over GPT- 4o on Nestful and BFCL v3 dataset, respectively.