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Structured Output, Function & Tool Calling — JSON Schema, Strict Mode, Agent Safety
How tool calling actually works in production — OpenAI-style function tools, JSON Schema constraints, strict structured outputs, parallel vs. sequential tools, and the auth/idempotency story interviewers expect. Connects the five GenAI planes from gateway design to eval and incident response.
Tool calling is an RPC boundary, not a string hack
Function (tool) calling exposes named procedures with a JSON Schema for arguments. The model emits a structured call (function name + JSON arguments) your runtime executes and feeds back. This is the API contract between an LM and the world — the same five GenAI planes apply: you design retrieval (which tools, which descriptions), generation (when the model is allowed to call), evaluation (schema pass rate, tool success rate, regression tests on calls), reliability (auth, injection, idempotency), and operations (cost per tool tier, P95, tracing).
Frontier APIs (OpenAI tools in Chat Completions / Responses API, Anthropic tools, Google function declarations) converge on: name, description (when to use), parameters (JSON Schema), and optional strict mode so arguments parse to the exact schema — not approximate JSON. For strict structured outputs and schema constraints, see OpenAI’s Structured Outputs guide (API documentation, not a paper) and your provider’s equivalent.
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