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To address intricate real-world tasks, there has been a rising interest intool utilization in applications of large language models (LLMs). To developLLM-based agents, it usually requires LLMs to understand many tool functionsfrom different tool documentation. But these documentations could be diverse,redundant or incomplete, which immensely affects the capability of LLMs inusing tools. To solve this, we introduce EASYTOOL, a framework transformingdiverse and lengthy tool documentation into a unified and concise toolinstruction for easier tool usage. EasyTool purifies essential information fromextensive tool documentation of different sources, and elaborates a unifiedinterface (i.e., tool instruction) to offer standardized tool descriptions andfunctionalities for LLM-based agents. Extensive experiments on multipledifferent tasks demonstrate that EasyTool can significantly reduce tokenconsumption and improve the performance of tool utilization in real-worldscenarios. Our code will be available at\url{https://github.com/microsoft/JARVIS/} in the future.

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction