Tool use: LLMs learning to reach for things

Tool use is how a language model stops talking in a vacuum and starts doing work. The model sees a list of tools it can call—search, code, calendars, APIs—and decides which one to reach for, sends the right arguments, then reads the result back in.

It's not new. ChatGPT has had it for a while; Anthropic's Claude and open-source models like Qwen and Llama 4 all support it. The trick is the schema: you tell the model what tools exist, what they take, and what they return. The model picks the right one and formats the call.

The analogy is a tía who cooks. When you ask her for dinner, she doesn't just guess. She checks the fridge, calls the abuelita for the recipe, pulls the pan from the cupboard. She uses the tools around her and feeds the result back.

Tool use matters because it turns a chatty model into a worker. Instead of writing a long answer from memory, it reaches for what it needs. You get more accurate answers with fewer hallucinations.

For us: if you're using a model that supports tools, check the settings—turn on search or code execution, and give it the right schemas. It'll do the work instead of pretending to know it.