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

Annotations already provide readOnly/destructive safety hints. The description adds valuable behavioral context beyond those: the response is 'concise' and the operation costs '~$0.03', which helps an agent anticipate output style and expense. No contradictions with annotations.

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 description is a single, front-loaded sentence with no filler. It packs the core purpose, model version, output character, and cost into minimal words, making it easy to parse quickly.

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?

For a simple one-parameter query tool with an output schema, the description is sufficiently complete: it states what to ask, which model, the response style, and cost. No additional behavioral or return-value details are needed given the low complexity and existing schema/annotations.

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 single parameter 'q' is fully documented in the schema as 'Your question', so the schema carries the semantic weight. The description does not add additional meaning about parameter syntax, accepted formats, or length constraints beyond what the schema already provides.

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 uses a specific verb ('Ask') with a clearly identified resource ('Google Gemini 3.1 Pro') and defines the output style ('concise answer'). This differentiates it from sibling tools like ask_gpt and ask_grok by naming the exact model variant and adding a cost signal.

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 using this tool when you need Google Gemini, but it does not explicitly state when to prefer it over alternatives or provide exclusions. The context is clear from the model name, but no direct comparison or selection guidance is given.

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