What is Tool Use?
Tool use is the capability for AI agents to invoke external functions, APIs, databases, or services to extend their capabilities beyond pure text generation. Rather than relying solely on the language model's parametric knowledge and reasoning, tool-using agents can search the web, query databases, perform calculations, execute code, retrieve documents, or interact with any system accessible through function calls. This dramatically expands what agents can accomplish and grounds their actions in real capabilities.
Modern tool use typically leverages function calling capabilities where the model is given descriptions of available tools (their names, purposes, parameters, and return types). When the model determines a tool would help accomplish a task, it generates a structured request specifying which tool to call and what arguments to provide. The system executes the tool, providing results back to the model, which can then use that information in its response or decide to call additional tools. This creates a loop of reasoning, tool use, and observation.
Tool use is fundamental to building practical AI agents that go beyond conversation to take actions and access real-time information. Examples include code interpreters that execute Python, search tools that query the web or databases, API clients that interact with external services, and calculator functions for precise arithmetic. Frameworks like LangChain, AutoGPT, and others provide extensive tool libraries and patterns for implementing tool-using agents. Effective tool use requires carefully describing tool capabilities, managing tool execution safely, and designing agent prompts that encourage appropriate tool selection and use.