Tool Use

Why Tool Use Matters

On their own, LLMs are text-in, text-out systems. They cannot browse the internet, run calculations precisely, read a file, or send an email. They are extraordinarily good at language and reasoning — but they have no hands.

Tool use (also called function calling) gives them hands.

How It Works

1. You define a set of tools — functions the model is allowed to call — by describing them in plain language and specifying their parameters.
2. When the model decides it needs to use a tool, it outputs a structured request instead of a direct answer: "call search_web with query = 'latest AI news'".
3. Your application executes the actual function and feeds the result back to the model.
4. The model incorporates the result and continues its response — or decides to call another tool.

The model never executes code directly. It requests; your system executes.

Common Tools in the Wild

• Web search — retrieve up-to-date information
• Code interpreter — run Python, do math, process data
• Database queries — look up customer records, product inventory
• Calendar and email — schedule meetings, send messages
• File read/write — read a document, save outputs

Tool Use is the Bridge to Agency

A model with tool use can accomplish multi-step tasks in the real world. This is why tool use is the foundational capability that separates a basic chatbot from a true AI agent. More tools means more real-world reach — and more responsibility for the developer to define what the model is allowed to touch.