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

A research team has introduced Trace-Free+, a novel curriculum learning framework designed to solve a critical bottleneck in LLM-based agent systems: poor tool descriptions. While most AI development focuses on fine-tuning the agents themselves, this work addresses the often-overlooked quality of the natural language descriptions and parameter schemas that agents use to understand available tools. The framework progressively transfers supervision from trace-rich training environments to trace-free deployment, teaching models to abstract reusable interface patterns and predict tool usage outcomes. This approach overcomes the limitations of existing methods that require execution traces—data frequently unavailable in new deployments or privacy-sensitive applications.

Experiments on StableToolBench and RestBench demonstrate that Trace-Free+ delivers consistent performance gains on unseen tools, shows strong cross-domain generalization, and maintains robustness as tool sets scale beyond 100 candidates. The researchers built a large-scale dataset of high-quality tool interfaces using a structured workflow across diverse tools to support their method. This breakthrough makes tool interface optimization a practical, deployable complement to agent fine-tuning, potentially unlocking more reliable AI assistants for enterprise applications where execution data is scarce or protected.