Using Tools: A Meeting Scheduler

Introduction

LLMs are great, but they are trained on public data sets. In some cases, you need the LLM to use data that’s not publicly available or that’s frequently changing. There are several ways to make such data available to LLMs:

• Tool/function calls
• Retrieval-augmented generation (aka RAG)
• MCP

In coding agents, you can also add skills.

In this post we’ll focus on function calling.

How Does It Work?

When interacting with an LLM, you can provide a description of available tools if the model supports tool calling. If the LLM reasons that the best answer is to use one of the tools, it will return a reply that contains a tool call with the parameters to use. Then you make the function call and return the answer back to the LLM.