AI Engineering/Phase 13/Lesson 02

Function Calling Deep Dive — OpenAI, Anthropic, Gemini

The three frontier providers converged on the same tool-call loop in 2024 and then diverged on everything else. OpenAI uses `tools` and `tool_calls`. Anthropic uses `tool_use` and `tool_result` blocks. Gemini uses `functionDeclarations` and unique-id correlation. This lesson diffs the three side by side so code that ships on one provider does not break when you port it.

Build 1.3 hr Python (stdlib, schema translators)

Prerequisites: Phase 13 · 01 (the tool interface)

#Learning Objectives

State the three shape differences between OpenAI, Anthropic, and Gemini function-calling payloads (declaration, call, result).
Translate one tool declaration across all three provider formats and predict where strict-mode constraints will differ.
Use tool_choice in each provider to force, forbid, or auto-pick tool calls.
Know the per-provider hard limits (tool count, schema depth, argument length) and the error signatures each one emits when limits are violated.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.