DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents
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. To bridge this gap, we conduct a large-scale empirical study on tool documentation for LLM agents. Our study reveals substantial heterogeneity in ...
| Authors | You Lu, Kun Zhang, Bihuan Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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