We explore Chain-of-Tools (CoTools), a groundbreaking tool learning method for frozen Large Language Models (LLMs). CoTools enables these models to effectively utilize a massive pool of tools, even those they haven't seen before, during Chain-of-Thought (CoT) reasoning. We delve into how CoTools leverages the semantic representation capabilities of LLMs to determine when and which tools to call. Discover how this novel approach outperforms existing baselines in both numerical reasoning and knowledge-based question answering, especially in scenarios with many unseen tools.