Imagine giving a powerful AI a toolbox. That's the exciting premise behind new research exploring how Large Language Models (LLMs) can learn to use external tools, transforming them from impressive text generators into practical problem-solvers. Traditionally, LLMs have been limited to the information they were trained on. This new research introduces "Automatic Tool Chain" (ATC), a framework that allows LLMs to access and use a variety of tools, much like a human using a search engine, calculator, or calendar. The LLM learns the function of each tool from its documentation and then programmatically strings these tools together to solve complex tasks. For example, an LLM could use a weather API, a map API, and a calendar to plan a trip, all without explicit human instruction for each step. This is a significant leap from previous approaches, which required manual design of each step in the tool-using process. ATC allows the LLM to plan more strategically and efficiently, especially for tasks requiring multiple steps. But what about teaching these LLMs to use *new* tools? Researchers have also developed a clever "black-box probing" method. The LLM essentially experiments with a new tool, figuring out its function through trial and error, and then documents its usage. This allows the LLM to expand its own toolbox without constant human intervention. This research opens doors to a future where AI can tackle real-world problems in a more flexible and autonomous way. Imagine LLMs managing your schedule, booking travel, or even conducting scientific experiments. While challenges remain, such as ensuring the accuracy of the LLM's tool usage and managing the complexity of interconnected tools, this research represents a significant step towards more practical and powerful AI.