This paper  introduces Play2Prompt, a new method for enhancing how large language models utilize external tools in zero-shot settings. This framework automatically refines tool documentation and generates usage examples by having the LLM "play" with the tools in a trial-and-error manner. Through this iterative process of interaction and self-reflection, Play2Prompt improves the LLM's ability to understand and correctly employ tools without relying on manual annotation or extensive prior knowledge. Experiments on real-world benchmarks demonstrate significant improvements in zero-shot tool performance compared to existing approaches, highlighting Play2Prompt's effectiveness and scalability for integrating specialized tools.