Tool Use (Function Calling)

A language model can reason and generate text, but it cannot check your inventory, send an email, or look up a flight on its own. Tool use changes that. It gives the model a list of available tools (described as function signatures with parameters) and lets it decide when to call them.

Here is how it works in practice. You define a set of tools, each with a name, description, and expected parameters. When the model receives a user request, it can choose to call one of these tools instead of (or in addition to) generating text. The system executes the tool call, returns the result to the model, and the model incorporates that result into its response.

For example, a customer support agent might have tools for looking up order status, checking return eligibility, and initiating a refund. When a customer asks "Where is my order?", the model calls the order lookup tool with the customer ID, gets the tracking information, and responds with it.

Tool use is what turns a chatbot into an agent. Without tools, the model can only talk about things. With tools, it can actually do things. This is the foundation of every production AI agent system.

The reliability of tool use depends on how well the tools are described and how clearly the model understands when to use each one. Good tool descriptions act like documentation for the model. Vague descriptions lead to incorrect tool calls.