Understanding tools. A prerequisite for an agent to use tools effectively is a comprehensive
understanding of the tools’ application scenarios and invocation methods. Without this understanding,
the process of the agent using tools will become untrustworthy and fail to genuinely enhance the
agent’s capabilities. Leveraging the powerful zero-shot and few-shot learning abilities of LLMs
[40; 41], agents can acquire knowledge about tools by utilizing zero-shot prompts that describe tool
functionalities and parameters, or few-shot prompts that provide demonstrations of specific tool usage
scenarios and corresponding methods [92; 326]. These learning approaches parallel human methods
of learning by consulting tool manuals or observing others using tools [94]. A single tool is often
insufficient when facing complex tasks. Therefore, the agents should first decompose the complex
task into subtasks in an appropriate manner, and their understanding of tools play a significant role in
TheRiseandPotentialofLargeLanguageModel BasedAgents