  To address intricate real-world tasks, there has been a rising interest in
tool utilization in applications of large language models (LLMs). To develop
LLM-based agents, it usually requires LLMs to understand many tool functions
from different tool documentation. But these documentations could be diverse,
redundant or incomplete, which immensely affects the capability of LLMs in
using tools. To solve this, we introduce EASYTOOL, a framework transforming
diverse and lengthy tool documentation into a unified and concise tool
instruction 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 tool utilization in real-world
scenarios. Our code will be available at
\url{https://github.com/microsoft/JARVIS/} in the future.
