Imagine an AI that can seamlessly use any tool, from design software to data analysis platforms, without explicit training. That's the promise of MetaTool, a groundbreaking approach to tool learning for Large Language Models (LLMs). Unlike current methods that rely on extensive demonstrations or annotations, MetaTool focuses on understanding the fundamental nature of tools – their causality and constraints. It achieves this through "meta-tasks," which are self-supervised exercises that involve predicting masked elements within a tool's execution process. This clever technique allows MetaTool to generate its own training data, eliminating the need for costly expert input. By incorporating meta-task data into task-oriented training, MetaTool equips LLMs with a deeper understanding of how tools work, enabling them to solve complex problems and even generalize to new tools they've never encountered before. The results are impressive, with MetaTool-enhanced LLMs rivaling the performance of cutting-edge models like ChatGPT in both tool-based planning and interactive scenarios. This innovation opens exciting possibilities for AI assistants that can navigate the complexities of real-world applications, from automating mundane tasks to tackling complex research problems. While challenges remain, MetaTool represents a significant leap towards truly general-purpose AI, capable of learning and adapting to any tool at its disposal.