Tool calling transforms AI from text generators into action takers. Models call defined functions to query databases, invoke APIs, and execute operations. Reliable tool calling requires careful function design, parameter validation, and error handling.
Function Design Principles
Well-designed tool functions have clear, specific purposes. Function names and descriptions guide model selection. Parameter schemas with detailed descriptions improve argument accuracy. Return values provide actionable information for next steps.
- Write detailed function descriptions explaining purpose and expected inputs
- Define parameter schemas with types, constraints, and examples
- Keep functions focused—single responsibility improves selection accuracy
- Return structured data models can understand and act upon
- Handle errors gracefully with informative error messages
Execution Patterns
Validate tool call parameters before execution. Implement timeouts preventing runaway operations. Log tool calls for debugging and audit purposes. Consider confirmation steps for high-impact actions. Build retry logic for transient failures.