In recent years, significant strides have been made in harnessing large language models (LLMs) to leverage various tools across different fields, which largely expands the application scope of LLMs. However, current research predominantly focuses on LLMs’ inherent tool exploitation skills from their training data, leading to higher costs when integrating new tools. Additionally, most studies concentrate on English models, leaving a scarcity of open-source resources for other languages. This study investigates the zero-shot generalization of LLMs in tool usage, with a focus on Chinese models. We introduce AtomTool, an open-source framework for tool acquisition in LLMs, along with a dataset of 16,000 Chinese entries. This work marks the first effort to evaluate zero-shot generalization in Chinese models and provides the initial open-source framework and dataset dedicated to tool acquisition in Chinese LLMs. Our experiments show AtomTool outperforms the closed-source models like ChatGPT in zero-shot generalization in most cases. We also propose a novel dataset construction method and evaluation framework, examining prompt design and tool quantity effects on model performance. Overall, our work establishes a solid foundation for advancing tool acquisition in Chinese LLMs.