Optimizing Agent Tool-Use via Trajectory-based Insight Evolution

Tool documentation is the critical interface bridging Large Language Model (LLM) agents and external environments. However, real-world documentation is often ambiguous or insufficient, leading to misaligned tool calls and task failures. While existing methods rely on parameter-efficient tuning or reinforcement learning to enhance tool-use capabilities, they incur substantial computational overhead and lack the agility to adapt to frequently updated toolkits. We propose OpTool , an evolutionary framework that treats tool documentation as an evolvable configuration. OpTool consists of three stages: (1) Contrastive Trajectory Generation, which explores diverse tool-use trajectories via contrastive beam search; (2) Trajectory-based Insight Extraction, which extracts tool-use insights through retrospective global credit assignment; and (3) Tool Documentation Evolution, which iteratively refines the documentation to mitigate information insufficiency. Experimental results across multiple domains demonstrate that OpTool significantly improves tool call accuracy and task success rates, providing a robust solution for tool-based LLM agents.