@inproceedings{yuan-etal-2025-easytool,
title = "{EASYTOOL}: Enhancing {LLM}-based Agents with Concise Tool Instruction",
author = "Yuan, Siyu and
Song, Kaitao and
Chen, Jiangjie and
Tan, Xu and
Shen, Yongliang and
Ren, Kan and
Li, Dongsheng and
Yang, Deqing",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.44/",
doi = "10.18653/v1/2025.naacl-long.44",
pages = "951--972",
ISBN = "979-8-89176-189-6",
abstract = "There has been a rising interest in utilizing tools in applications of autonomous agents based on large language models (LLMs) to address intricate real-world tasks. To develop LLMbased agents, it usually requires LLMs to understand many tool functions from different tool documentations. However, these documentations could be diverse, redundant, or incomplete, which immensely affects the capability of LLMs in using tools. Current LLMs exhibit satisfactory instruction-following capabilities based on instruction-following fine-tuning process. Motivated by this, in this paper, we introduce EASYTOOL, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction to fully leverage instruction-following capabilities of LLMs for easier tool usage. EASYTOOL purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i.e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents. Extensive experiments on multiple different tasks demonstrate that EASYTOOL can significantly reduce token consumption and improve the performance of LLM-based agents on tool utilization in real-world scenarios. Our code is available in supplemental materials. Our code is available at https://github.com/microsoft/JARVIS/tree/main/easytool."
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<abstract>There has been a rising interest in utilizing tools in applications of autonomous agents based on large language models (LLMs) to address intricate real-world tasks. To develop LLMbased agents, it usually requires LLMs to understand many tool functions from different tool documentations. However, these documentations could be diverse, redundant, or incomplete, which immensely affects the capability of LLMs in using tools. Current LLMs exhibit satisfactory instruction-following capabilities based on instruction-following fine-tuning process. Motivated by this, in this paper, we introduce EASYTOOL, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction to fully leverage instruction-following capabilities of LLMs for easier tool usage. EASYTOOL purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i.e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents. Extensive experiments on multiple different tasks demonstrate that EASYTOOL can significantly reduce token consumption and improve the performance of LLM-based agents on tool utilization in real-world scenarios. Our code is available in supplemental materials. Our code is available at https://github.com/microsoft/JARVIS/tree/main/easytool.</abstract>
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%0 Conference Proceedings
%T EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
%A Yuan, Siyu
%A Song, Kaitao
%A Chen, Jiangjie
%A Tan, Xu
%A Shen, Yongliang
%A Ren, Kan
%A Li, Dongsheng
%A Yang, Deqing
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F yuan-etal-2025-easytool
%X There has been a rising interest in utilizing tools in applications of autonomous agents based on large language models (LLMs) to address intricate real-world tasks. To develop LLMbased agents, it usually requires LLMs to understand many tool functions from different tool documentations. However, these documentations could be diverse, redundant, or incomplete, which immensely affects the capability of LLMs in using tools. Current LLMs exhibit satisfactory instruction-following capabilities based on instruction-following fine-tuning process. Motivated by this, in this paper, we introduce EASYTOOL, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction to fully leverage instruction-following capabilities of LLMs for easier tool usage. EASYTOOL purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i.e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents. Extensive experiments on multiple different tasks demonstrate that EASYTOOL can significantly reduce token consumption and improve the performance of LLM-based agents on tool utilization in real-world scenarios. Our code is available in supplemental materials. Our code is available at https://github.com/microsoft/JARVIS/tree/main/easytool.
%R 10.18653/v1/2025.naacl-long.44
%U https://aclanthology.org/2025.naacl-long.44/
%U https://doi.org/10.18653/v1/2025.naacl-long.44
%P 951-972
Markdown (Informal)
[EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction](https://aclanthology.org/2025.naacl-long.44/) (Yuan et al., NAACL 2025)
ACL
- Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, and Deqing Yang. 2025. EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 951–972, Albuquerque, New Mexico. Association for Computational Linguistics.