AI Engineering/Phase 13/Lesson 05

Tool Schema Design — Naming, Descriptions, Parameter Constraints

A correct tool fails silently when the model cannot tell when to use it. Naming, descriptions, and parameter shapes drive 10 to 20 percentage-point swings in tool-selection accuracy on benchmarks like StableToolBench and MCPToolBench++. This lesson names the design rules that separate a tool a model picks reliably from a tool a model mis-fires.

Learn 45 min Python (stdlib, tool schema linter)

Prerequisites: Phase 13 · 01 (the tool interface), Phase 13 · 04 (structured output)

#Learning Objectives

Write a tool description using the "Use when X. Do not use for Y." pattern, under 1024 characters.
Name tools in a way that is stable, snake_case, and unambiguous across a large registry.
Choose between atomic tools and a single monolithic tool for a given task surface.
Run a tool-schema linter against a registry and fix the findings.

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