What to take into an interview
- Treat a tool call as a proposal that application code must authorize.
- Keep schema definitions precise, narrow, and versioned with handlers.
- Return structured results and errors that support the next model step.
Understand the tool calling lifecycle
The application sends tool definitions with a model request. The model may return one or more structured tool calls. Your code parses them, validates arguments, checks authorization, executes approved handlers, and sends tool results into the next model turn.
The model does not execute your function and structured output does not remove the need for validation. This distinction is central to safe design and a common interview question.
Write useful function schemas
Use a specific function name and description that state when the tool should be called. Describe parameters in domain language, mark required fields, constrain enumerated values, and avoid optional fields that silently change consequential behavior.
- Keep the JSON schema synchronized with the executable handler.
- Use server-side defaults for policy decisions rather than model choices.
- Version incompatible contract changes and retain trace visibility.
- Limit the available tool set to the current user intent.
Execute with application controls
After schema validation, apply authentication, tenant isolation, resource limits, and business rules. For write operations, use confirmations or policy checks appropriate to the consequence of the action.
Parallel calls may reduce latency for independent reads, but preserve ordering when one result changes the inputs or authorization of another. Protect repeated writes with idempotency.
Test errors and model recovery
Return concise structured tool results that distinguish success, validation failure, permission failure, transient dependency failure, and permanent domain failure. The next model step should receive enough information to recover without seeing secrets or internal stack traces.
Test malformed calls, unknown tools, extra fields, retries, duplicate identifiers, partial parallel failures, and a model that continues requesting tools after the budget is exhausted.
Hands-on practice
Related coding problems
Generate a JSON Schema for a Tool
Create a model-readable contract from a Python function.
Open problemDerive Tool Params from Type Hints
Map typed function parameters into schema properties.
Open problemExecute Parallel Tool Calls with Traceable Results
Coordinate multiple calls while preserving result alignment.
Open problemBuild the foundation
Related curriculum tracks
Common questions
Frequently asked questions
Does OpenAI tool calling execute functions automatically?
No. The model produces structured call requests. Your application decides whether and how to validate, authorize, execute, and return each result.
Is structured output guaranteed to be safe?
No. Structure makes parsing more reliable, but values still require schema validation, authorization, domain checks, and execution limits.
Should tool errors be shown to the model?
Provide a sanitized, structured error when it can support recovery. Do not expose credentials, stack traces, private data, or implementation details the model does not need.
Practice next
Build a safe tool execution boundary
Practice generating contracts, validating typed arguments, and coordinating multiple calls before connecting model-selected actions to real systems.