This paper introduces AutoTools, a novel framework designed to empower large language models (LLMs) to function as automated tool agents. This system enables LLMs to automatically transform tool documentation into callable functions and subsequently integrate these functions into executable programs to solve practical tasks. The authors identify limitations in previous manual approaches for tool utilization by LLMs and propose AutoTools as a more scalable and flexible solution. Furthermore, they present AutoTools-Learning, a training approach using synthetic data to enhance the tool-use expertise of LLMs, particularly those with fewer parameters, across tasks like documentation understanding and function programming. The efficacy of AutoTools is validated through experiments on existing and a newly created challenging benchmark, demonstrating its superiority in automating the tool-use workflow for LLMs.

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