Integrating external tools enables Large Language Models (LLMs) to interact with real-world environments and solve complex tasks. Given the growing scale of available tools, effective tool retrieval is essential to mitigate constraints of LLMs' context windows and ensure computational efficiency. Existing approaches typically treat tool retrieval as a traditional ad-hoc retrieval task, matching user queries against the entire raw tool documentation. In this paper, we identify three fundamental challenges that limit the effectiveness of this paradigm: (i) the incompleteness and structural inconsistency of tool documentation; (ii) the significant semantic and granular mismatch between user queries and technical tool documents; and, most importantly, (iii) the multi-aspect nature of tool utility, that involves distinct dimensions, such as functionality, input constraints, and output formats, varying in format and importance. To address these challenges, we introduce Multi-Field Tool Retrieval, a framework designed to align user intent with tool representations through fine-grained, multi-field modeling. Experimental results show that our framework achieves SOTA performance on five datasets and a mixed benchmark, exhibiting superior generalizability and robustness.

翻译：集成外部工具使大型语言模型（LLMs）能够与现实世界环境交互并解决复杂任务。鉴于可用工具规模日益增长，有效的工具检索对于缓解LLMs上下文窗口的限制并确保计算效率至关重要。现有方法通常将工具检索视为传统的即席检索任务，将用户查询与完整的原始工具文档进行匹配。本文中，我们识别了限制该范式有效性的三个根本挑战：（i）工具文档的不完整性与结构不一致性；（ii）用户查询与技术工具文档之间显著的语义与粒度失配；以及最关键的是，（iii）工具效用的多维度特性，其涉及功能、输入约束和输出格式等不同维度，这些维度在形式与重要性上存在差异。为应对这些挑战，我们提出了多领域工具检索框架，该框架通过细粒度的多领域建模实现用户意图与工具表征的对齐。实验结果表明，我们的框架在五个数据集和一个混合基准上取得了最先进的性能，展现出卓越的泛化能力与鲁棒性。