Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already set readOnlyHint=false and openWorldHint=true, so external/LLM behavior is known. The description adds the concrete cost (~$0.03) and the concise-answer trait, which are useful beyond the annotations and help set expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The entire description is one short sentence plus a cost qualifier, front-loading the core action and result. Every word adds value; there is no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (one parameter, output schema present, annotations available), the description is adequately complete. It adds cost and output-style context, while return structure is presumably provided by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema fully covers the single parameter 'q' with description 'Your question', so the baseline applies. The description's 'any question' adds little beyond the schema, but no parameter meaning is missing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly names the action ('Ask OpenAI GPT-5.2'), the resource (the model), and the expected result ('concise answer'). Mentioning GPT-5.2 distinguishes it from sibling tools like ask_gemini and ask_grok.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies it is for general questions where a concise answer from GPT-5.2 is desired, and the cost hint suggests lightweight use. However, it does not explicitly state when to prefer this over ask_ai, ask_ai_pro, or other alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.