Function Calling
A feature that connects an LLM to interpret a user's request and select and call pre-registered external functions or APIs.
Detailed explanation
Why it matters in tool selection
Support for Function Calling is the dividing line between simple chatbots and AI agents that handle actual tasks. To automate tasks by integrating with external systems, the model must be able to structure function calls reliably. When choosing a tool, it is recommended to check whether it can call multiple functions simultaneously, enforce call argument formats with schemas, and implement retry/validation logic in case of call failures.
What to check when choosing a tool
- Can it enforce function arguments with a JSON schema to reduce formatting errors?
- Can it call multiple functions in parallel within a single response?
- Can it pass the execution results back to the model to continue subsequent reasoning?
- Are there verification and retry mechanisms for incorrect calls or missing arguments?
Real-world Application Examples
When receiving a request like "Find flights for my business trip to Busan next week," the model, instead of hallucinating an answer, designates the search_flights function to be called and returns it populated with departure, destination, and date arguments. Once the application calls the actual flight API and passes the results back, the model formats the information into a human-readable response.