Tool Use

Tool use is the mechanism that transforms a language model from a pure text processor into an agent capable of interacting with the real world. When a model supports tool use, the application developer defines a set of available functions — their names, descriptions, parameters, and return types — and includes these definitions in the model's context. When generating a response, the model can recognize that calling one of these tools would help it answer the question better, generate a structured tool call (a JSON object specifying the function name and arguments), and pause generation while the application executes the function and returns the result. The model then incorporates the result into its response. This cycle can repeat multiple times in a single interaction: a model answering "What's our ARR growth year-over-year?" might call a database query tool, get the current ARR, call it again for the previous year, and then compute and explain the comparison.

The practical impact of tool use is the difference between an AI that knows what it knew when it was trained and an AI that can actively retrieve, create, and act on current information. Common tool categories include: retrieval tools (web search, database queries, document retrieval), action tools (sending emails, creating calendar events, writing files, calling APIs), computation tools (code execution, mathematical calculations, data processing), and communication tools (posting to Slack, GitHub, CRM systems). Tool use is the foundation of what makes AI agents meaningfully autonomous — an agent without tool access can only reason about information it already has in context; an agent with rich tool access can investigate, act, and respond to dynamic real-world state.

For B2B teams, designing the tool interface is a critical architectural decision when building AI-powered applications. Tools should have clear, descriptive names and descriptions (the model chooses which tool to call based on the description), well-typed parameters with helpful descriptions, and predictable return values. Poor tool design — ambiguous names, overlapping capabilities, inconsistent return formats — causes models to use tools incorrectly or not at all. The security implications of tool use also require careful design: a tool that can delete records, send messages on behalf of users, or access sensitive systems should have appropriate authorization checks, because the AI calling a tool is executing real-world actions with potentially significant consequences.