How tool use works
On its own a model only writes. It cannot look up today’s stock, read your ad account or send anything. Tool use is the arrangement that changes that: you describe a set of actions the model is allowed to request — fetch a page, search a knowledge base, look up an order, create a CRM record — and when it decides one is needed, it asks for it. Your own code carries out the request, returns the result, and the model continues with real information in front of it.
The loop is what matters. Ask, act, read the result, decide again. The model never touches your systems directly; it only ever asks, and your side decides whether to comply. Function calling is the usual technical mechanism for that request, and MCP is one way of describing the available tools in a standard form.
Why tool use matters
It is the difference between a model that sounds knowledgeable and one that is useful. A chatbot without tools can describe your services in general terms; with a lookup tool it can answer whether a particular item is in stock or where an order has reached. That is the point at which customers stop treating it as decoration.
It also cuts the main source of invented answers. A model that can check will usually check, rather than fill the gap from memory, so grounding a system in your real data does more for reliability than any amount of instruction not to make things up.
Where tool use goes wrong
The first mistake is handing over broad permissions because it is quicker. A tool that can read your whole CRM is a bigger exposure than one that can look up a single record by identifier, and the narrower version does the job just as well.
The second is confusing reading with writing. Fetching information is recoverable; sending a message, changing a price or deleting a record is not. Those deserve a separate decision and usually a person.
The third is a poor description. The model chooses a tool from the words you use to describe it, so a vague name and a thin explanation produce a model that calls the wrong thing, or calls nothing when it should. And a tool that fails silently is worse than one that errors, because the model will simply carry on without the fact it was missing.
How to act on it
Grant the least access that works. Start with read-only tools, scoped as tightly as the system allows, and add anything that writes only when the workflow has proved itself over real runs. Describe each tool plainly, including when it should not be used, and make failures loud so the model and the log both know a call did not work.
Keep records of every call and its result. When an answer turns out wrong, the log usually shows a tool returned nothing rather than the model inventing something, and that is a far easier fault to fix.