If a large language model on its own is a brain in a jar, tool use is what gives it hands. Instead of only predicting text, the model calls an external function, queries an API, searches a database, or triggers a piece of software, then folds the result back into its reasoning. It decides which tool to reach for, calls it, reads what comes back, and keeps going.
Tool use is the mechanism that turns a chatbot into an AI agent. It is also what a shared standard like the Model Context Protocol exists to make safe and repeatable, giving models a consistent way to discover and call the tools around them rather than each vendor reinventing the wiring.
For a creative team, tool use is the difference between an assistant that drafts a caption and one that pulls the live brand guidelines, checks the campaign calendar, generates three on-brief variations, and files them in the right client workspace. A studio asking an agent to "resize this campaign for six markets" is relying on tool use behind the scenes: the model reaches for an image tool, a translation step, and the asset library in turn. The output is only as good as the tools it can reach and the context it is handed.
Give a model too many tools, or badly described ones, and it fumbles the choice. Give it a tight, well-documented set plus clean context and it starts doing real work instead of talking about it. That pairing, capable tools and disciplined context, is where practical AI for agencies actually lives.