Tool use (also called function calling) is the ability of an AI model to recognize when it needs external capabilities — such as databases, APIs, calculators, or enterprise systems — and automatically invoke the right tool with the correct parameters to complete a task. Instead of generating a best-guess answer from its training data, the model calls out to real systems for real-time, accurate information and takes actions in the world.
Think of the difference between asking someone a question from memory versus watching them pick up the phone, call an expert, and relay the answer. Without tool use, a language model can only work with what it was trained on — which may be outdated, incomplete, or wrong for your specific context. With tool use, the model can check your CRM for the latest customer data, query your inventory system for current stock levels, run a calculation in a spreadsheet, or create a ticket in your project management tool.
For enterprise leaders, tool use is the capability that transforms AI from a sophisticated text generator into a system that can actually do work. It's what makes the difference between an AI that says "you should check your inventory levels" and one that checks inventory levels, finds the three products below reorder threshold, and drafts a purchase order. Every major model provider (OpenAI, Anthropic, Google) now supports function calling, and it's the foundational mechanism behind agentic AI — agents without tools are just chatbots with ambition.
Imagine a personal assistant who knows how to use every tool in your office — they can look things up in your filing cabinet, make calls on your behalf, send emails, run numbers on a calculator, and update your calendar. You don't have to explain how each tool works every time; you just say what you need, and they pick the right tool, use it correctly, and bring you the result. Tool use in AI works the same way.
When an AI model supports tool use, developers define a set of available tools — each described by a name, a purpose, and the parameters it accepts (similar to an API specification). When the model receives a request that requires external information or action, it generates a structured tool call (typically JSON) specifying which tool to use and what inputs to provide. The application executes the actual tool call, retrieves the result, and feeds it back to the model, which then incorporates the real-world data into its response. The model itself never directly accesses external systems — the application mediates all tool calls, which is critical for security and access control in enterprise environments.
A retail company deployed tool use to connect their customer service AI to five backend systems: order management, inventory, shipping tracking, returns processing, and loyalty points. When a customer asks "Where's my order?", the AI calls the order lookup tool with the customer ID, retrieves the tracking number, calls the shipping API for current status, and responds with specific delivery details — all in under 3 seconds. Customer service resolution time dropped 60% for routine inquiries.
In financial services, analysts use AI models with tool use to automate research workflows. The model calls a financial data API to pull current market data, executes calculations in a Python environment, queries an internal knowledge base for company-specific analysis frameworks, and generates formatted reports. What took an analyst 4 hours of manual data gathering and formatting now takes 15 minutes of review and refinement.
These use cases share a common pattern: tool use is most valuable when the task requires combining AI reasoning with access to real-time, organization-specific data. Any workflow where someone currently copies data between systems, runs lookups, or performs routine calculations is a strong candidate for tool-use-enabled AI.
Tool use in AI has conceptual roots in early expert systems of the 1980s, which used rule-based engines to query databases and execute procedures. However, modern tool use emerged in 2023 when OpenAI introduced function calling in the GPT-3.5 and GPT-4 APIs, allowing developers to define functions that the model could invoke during conversation. This was a watershed moment — it meant language models could go beyond generating text to taking structured actions.
Anthropic, Google, and other providers quickly followed with their own tool use implementations. By 2024, function calling became a standard capability expected of any enterprise-grade language model. The open-source community developed standards like the Model Context Protocol (MCP) to create interoperable tool definitions across model providers. The current trajectory is toward more sophisticated tool use: models that can chain multiple tool calls autonomously, learn to use new tools from documentation alone, and maintain stateful interactions with complex enterprise systems. This evolution is what underpins the agentic AI movement — autonomous agents are fundamentally language models with robust tool use capabilities and planning logic.
Tool use and function calling are the capabilities that bridge the gap between AI as a text generator and AI as a business system. By enabling language models to invoke external APIs, query databases, run calculations, and interact with enterprise software, tool use transforms AI from "interesting demo" to "production workflow." It's the foundational mechanism behind AI agents, copilots, and any AI system that needs to access real-time data or take actions beyond generating text.
Enterprise leaders should evaluate AI platforms based on their tool use capabilities: How many tools can be defined? How reliably does the model select the correct tool? What security and access controls govern tool execution? And critically, how easily can existing enterprise APIs be exposed as tools without rebuilding them? Organizations that invest in tool-use infrastructure now are building the foundation for every agentic AI application they'll deploy in the future.
