Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

• URL: http://arxiv.org/abs/2602.20426v1
• Date: Mon, 23 Feb 2026 23:50:24 GMT
• Title: Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
• Authors: Ruocheng Guo, Kaiwen Dong, Xiang Gao, Kamalika Das,
• Abstract summary: We propose a curriculum learning framework that transfers supervision from trace-rich settings to trace-free deployment.<n> Experiments show consistent gains on unseen tools, strong cross-domain generalization, and robustness as the number of candidate tools scales to over 100.
• Score: 21.666294374943178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of LLM-based agents depends not only on the agent itself but also on the quality of the tool interfaces it consumes. While prior work has focused heavily on agent fine-tuning, tool interfaces-including natural language descriptions and parameter schemas-remain largely human-oriented and often become a bottleneck, especially when agents must select from large candidate tool sets. Existing approaches to improving tool interfaces rely on execution traces, which are frequently unavailable in cold-start or privacy-constrained settings, and typically optimize each tool independently, limiting scalability and generalization to unseen tools. We propose Trace-Free+, a curriculum learning framework that progressively transfers supervision from trace-rich settings to trace-free deployment, encouraging the model to abstract reusable interface-usage patterns and tool usage outcomes. To support this approach, we construct a large-scale dataset of high-quality tool interfaces using a structured workflow over a diverse collection of tools. Experiments on StableToolBench and RestBench show consistent gains on unseen tools, strong cross-domain generalization, and robustness as the number of candidate tools scales to over 100, demonstrating that tool interface optimization is a practical and deployable complement to agent fine-tuning.

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