Tool Use

Tool use is an LLM grounding its answer in the result of an external tool instead of leaning on training data alone. The motivation is concrete: LLMs do not know anything after their training cutoff, they cannot give you exact real-time prices or stock, and they cannot trigger side effects like a booking or a payment. Add tools and 'an answering chatbot' becomes 'an agent that gets work done'. OpenAI, Anthropic and Google all expose this through standardised interfaces — tool use or function calling.

The mechanics are surprisingly simple. You describe the tools to the model — name, arguments, short description — and during generation the model emits a structured tool call as JSON. The client intercepts it, runs the real function, feeds the result back into the model, and the model uses that result to produce the final answer. This is the 'Action' step of the ReAct loop (Thought → Action → Observation), and almost every modern agent is a variation on the same pattern.

For Villion and marketers, tool use is the substrate underneath 'an AI that calls your site's API and runs through to checkout'. Catalog lookup, inventory check, payment, shipment tracking — each one becomes a single tool call inside the agent. Your API spec, auth model and error responses become part of the marketing stack. Vague tool descriptions mean the agent skips your tool. Inconsistent responses mean transactions break mid-flow. Your API documentation is now nearly as load-bearing as your ad copy.

It also helps to see where tool use sits relative to neighbours. Tool use is the low-level interface. On top of it sit standards like MCP, and on top of those sit frameworks like LangChain and the OpenAI Assistants API. MCP standardises how tools are exposed; tool use is the act of the model actually calling them. If MCP is the harbour, tool use is the act of docking and unloading the cargo.

The most common production mistake is assuming more tools is always better. Past about ten tools, accuracy collapses — the model starts misrouting calls. Fewer tools with shorter, sharper names and argument schemas move accuracy more than any other lever. In Korea there is also a language choice to make. Models tend to respond better to English tool descriptions, so if you target both global and domestic agents, an English description with a Korean supplementary note is a safe default to start from.