What is Tool Use?

Tool use (also called function calling or tool calling) is the capability of AI models to invoke external tools, APIs, and functions to accomplish tasks that go beyond text generation. Instead of simply generating text responses, a model with tool use capabilities can search the web, execute code, query databases, send emails, manipulate files, and interact with virtually any software system through defined tool interfaces. This capability is fundamental to building AI agents and practical AI-powered applications.

The mechanism of tool use works through a structured protocol. The developer defines available tools by providing descriptions, parameter schemas, and function signatures. When the model determines that a tool would help answer a query, it generates a structured tool call (typically JSON) specifying which tool to invoke and with what parameters. The application executes the tool call, returns the result to the model, and the model incorporates the result into its response. This cycle can repeat multiple times as the model chains tool calls to accomplish complex objectives.

Tool use is employed across a wide range of applications. AI coding assistants use tools to read files, run tests, and execute terminal commands. Research assistants use web search and document retrieval tools to gather information. Customer service bots use tools to look up order status, process returns, and update account information. Data analysis agents use tools to run SQL queries, generate charts, and export reports. The Model Context Protocol (MCP), introduced by Anthropic, is an emerging open standard for defining tool interfaces that work across different AI models and providers.

Effective tool use design requires careful consideration of tool descriptions (clear enough for the model to know when and how to use each tool), parameter validation (ensuring the model provides valid inputs), error handling (what happens when a tool call fails), security (preventing unauthorized actions), and rate limiting (controlling how many tool calls the model can make). The quality of tool descriptions has a major impact on how reliably models select and use the right tools.