Tool definition generator

When an agent calls the wrong tool or passes garbage arguments, the instinct is to blame the model. Far more often, the tool definition is the problem: a vague description, an enum left as a free-string, a required field marked optional. This skill generates tool/function schemas that are written for the model, so correct calls are easy and invalid ones are impossible.

When to use this skill

• Wiring existing functions or API endpoints into an agent's tool-calling loop.
• An agent picks the wrong tool, omits required arguments, or passes malformed values.
• Standardizing tool schemas across an agent codebase.

Instructions

1. Read the source of truth. Derive the schema from the actual function signature, types, and docstring (or an OpenAPI spec) — never hand-wave argument names. Inspect call sites to learn real usage.
2. Name and describe for the model, not the compiler. The tool name and description are prompt surface: state plainly what it does and when to use it (and when not to). Ambiguous descriptions cause more bad calls than a weak system prompt.
3. Type every argument precisely. Use JSON Schema types, mark fields required honestly (don't mark everything optional to be safe — that invites omissions), and add per-argument descriptions with units and formats ("ISO 8601 date", "USD cents").
4. Constrain with enums and bounds. Replace free-strings with enum where the set is known, add min/max and patterns where they apply. A constrained schema makes an invalid call structurally impossible rather than merely discouraged.
5. Keep the surface tight. Fewer, well-scoped tools beat many overlapping ones. If two tools are easily confused, disambiguate their descriptions or merge them.
6. Emit in the target format. Produce the schema in the shape the framework expects (OpenAI/Anthropic tool format, or the agent SDK's decorator), and verify it validates.

[!TIP] The description is doing prompt engineering. "Refund a charge. Use only after confirming the charge exists and the amount; do not use for subscription cancellations." prevents more misfires than any amount of system-prompt nagging.

[!NOTE] This generates the interface the model calls. The runtime still needs error handling and (for consequential actions) a human-in-the-loop-gate — a good schema reduces bad calls but doesn't replace guardrails.

Output

Validated tool/function schemas in the target format: precise types, honest required fields, model-facing descriptions, and enums/bounds that constrain inputs — ready to drop into the agent's tool list.