What Is Function Calling? AI Tool Calling Explained

If you have used ChatGPT, you know it is excellent at writing emails, summarizing text, and answering questions. But what happens if you ask it to check the current weather in Tokyo, or to refund a customer’s order in Shopify?

Historically, language models could not do these things. They could only generate text based on the static data they were trained on.

To bridge the gap between "generating text" and "taking action," AI researchers introduced a breakthrough feature called Function Calling (also known as Tool Calling).

In this guide, we will explain exactly what function calling is, how it works in plain English, and why it is the fundamental building block of every modern AI agent.

The Limitation of Standard Language Models

Before function calling, language models were completely isolated from the outside world.

If you built a customer support chatbot and a user asked, "Where is my package?", the AI could only reply with something generic like: "I am an AI and do not have access to your shipping information."

To fix this, developers tried to "trick" the AI. They would write instructions like: "If the user asks for shipping status, reply with the exact word [CHECK_SHIPPING] and nothing else." The developer's software would then look for that specific word, trigger a database search, and feed the result back to the AI.

This workaround was incredibly fragile. The AI would frequently get confused, ignore the instructions, or change the formatting, breaking the entire software system.

What is Function Calling?

Function calling is a feature built directly into modern AI models (like OpenAI's GPT-4 or Anthropic's Claude) that allows them to reliably output structured data that a computer program can understand, instead of just outputting conversational text.

In software development, a "function" is simply a block of code designed to perform a specific task.

• get_weather(location) is a function.
• refund_order(order_id) is a function.
• search_database(query) is a function.

When developers build an AI application today, they provide the AI with a list of available functions (or tools) it is allowed to use, along with a description of what each tool does.

If the user asks a question that requires outside information, the AI stops generating conversational text. Instead, it "calls the function." It tells the developer's software: "I need you to run the get_weather function, and the location you should use is 'Tokyo'."

How Function Calling Works (Step-by-Step)

Let's look at a real-world example. Imagine you are interacting with an AI assistant built for an e-commerce store running on Shopify.

Step 1: The Request You type into the chat: "Please refund my order #98765."

Step 2: The AI Analyzes the Tools The AI looks at the list of tools the developer gave it. It sees a tool called refund_shopify_order which requires an order_number.

Step 3: The Function Call The AI realizes it cannot do this with text alone. It stops chatting and sends a structured data request (usually in JSON format) back to the application: { "tool": "refund_shopify_order", "order_number": "98765" }

Step 4: The Application Executes the Action The AI itself does not connect to Shopify. The AI just output the instruction. The developer's actual software receives that instruction, securely connects to the Shopify API, processes the refund, and gets a "Success" message back.

Step 5: The AI Replies to You The developer's software feeds that "Success" message back to the AI. The AI then switches back into conversational mode and replies to you: "I have successfully refunded order #98765. The money should appear in your account in 3-5 days."

Why This Changes Everything

Function calling is the bridge between thinking and doing. It is what transforms a standard chatbot into an autonomous AI agent.

Because of function calling, AI can now control existing software.

• It allows an AI to control a web browser to scrape a Wikipedia page.
• It allows an AI to query a massive PostgreSQL database to analyze financial records.
• It allows an AI to control a smart home system to turn off your Philips Hue lights.

Without function calling, the AI is just a conversationalist. With function calling, the AI becomes an active participant in your workflow.

Standardizing the Tools: The Role of MCP

As function calling became popular, a new problem emerged: developers had to write custom code to connect the AI to every single tool. If they wanted the AI to check GitHub, they had to write a custom GitHub tool. If they wanted it to check Slack, they had to write a custom Slack tool.

This is exactly why the Model Context Protocol (MCP) was created.

Instead of writing custom functions for every single app, MCP provides a universal standard. If an application (like Slack or Notion) supports MCP, the AI can instantly "call its functions" without the developer needing to write custom integration code.

(Want to learn more? Read our guide: MCP 101: Model Context Protocol Explained for Non-Technical Users).

Summary

• Standard AI just predicts the next word to generate conversational text.
• Function calling (or tool calling) allows the AI to output structured commands instead of just text.
• The developer's software runs those commands to interact with external APIs (like Shopify, Spotify, or Netflix).
• The results are fed back to the AI, allowing it to complete complex, real-world tasks.
• This specific capability is what makes modern AI agents possible.