arXiv:2608.10037v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents. Existing studies mainly focus on improving the tool-use capabilities of LLM agents, while largely treating tool documentation as a fixed input. Although several recent works attempt to optimize tool documentation through rewriting or compression, little is known about how the information contained in tool documentation affects agent performance across different settings. T
Optimizing tool documentation for agents reduces integration friction, lowers failure rates, and speeds iteration when deploying LLM tool-use in changing enterprise environments.
Published to Cognify News · Week 33, 2026