<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tool on</title><link>https://www.ardanlabs.com/categories/tool/</link><description>Recent content in Tool on</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 16 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.ardanlabs.com/categories/tool/index.xml" rel="self" type="application/rss+xml"/><item><title>Using Tools: A Meeting Scheduler</title><link>https://www.ardanlabs.com/blog/2026/03/using-tools-a-meeting-scheduler/</link><pubDate>Mon, 16 Mar 2026 00:00:00 +0000</pubDate><guid>https://www.ardanlabs.com/blog/2026/03/using-tools-a-meeting-scheduler/</guid><description>&lt;h3 id="introduction"&gt;Introduction&lt;/h3&gt;&#10;&lt;p&gt;LLMs are great, but they are trained on public data sets. In some cases, you need the LLM to use data that&amp;rsquo;s not publicly available or that&amp;rsquo;s frequently changing. There are several ways to make such data available to LLMs:&lt;/p&gt;&#10;&lt;ul&gt;&#10;&lt;li&gt;Tool/function calls&lt;/li&gt;&#10;&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Retrieval-augmented_generation"&gt;Retrieval-augmented generation&lt;/a&gt; (aka RAG)&lt;/li&gt;&#10;&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Model_Context_Protocol"&gt;MCP&lt;/a&gt;&lt;/li&gt;&#10;&lt;/ul&gt;&#10;&lt;p&gt;In coding agents, you can also add skills.&lt;/p&gt;&#10;&lt;p&gt;In this post we&amp;rsquo;ll focus on function calling.&lt;/p&gt;&#10;&lt;h3 id="how-does-it-work"&gt;How Does It Work?&lt;/h3&gt;&#10;&lt;p&gt;When interacting with an LLM, you can provide a description of available tools if the model supports tool calling.&#10;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.&lt;/p&gt;</description></item></channel></rss>