LLM Tool Use

Large language model tool use examines how models decide when tools are needed, choose among them, provide valid arguments, and use returned results to complete tasks. Common approaches include prompting frameworks such as ReAct, supervised fine-tuning and reinforcement learning on tool-call trajectories, retrieval and filtering for large tool catalogs, and planning with execution feedback or memory. Benchmarks such as BFCL, AppWorld, and MCP-based evaluations test structured calls, multi-step workflows, and real-world tool interactions. A central challenge is making tool use reliable and efficient: tools can improve computation and access to current information, but unnecessary calls, bad arguments, misleading results, and failures in long-horizon reasoning can make performance worse.