AI models use tool calling to trigger external actions via structured requests

The article explains how tool calling works by breaking down its anatomy, workflow, and key design principles. A tool definition includes a name, description, input schema, and output contract, often using JSON Schema. The tool-calling loop involves the application providing context, the model choosing a tool, the application validating and executing the request, and the model observing the result before producing a final answer. Tool choice can be automatic, required, constrained, or prohibited, depending on the workflow. The article also compares tool calling to structured output and APIs, emphasizing that tool calling is a decision layer that selects operations and supplies arguments.

The article highlights best practices for designing good AI tools, such as clear purpose, small typed inputs, useful outputs, idempotency, and narrow permissions. It also discusses common failures like wrong tool selection, invalid arguments, hallucinated results, and unsafe chaining. The article concludes by emphasizing the importance of evaluation metrics, logging, and human approval for high-risk actions.