ask

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

Since no annotations are provided, the description carries full burden. It discloses the no-web-search limitation, inability to retrieve latest/real-time info, and default model context. It also adds transparency about model pricing and trade-offs. Missing details like rate limits or output format, but the key limitations are covered.

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 front-loaded with the core purpose, then uses compact, structured lists for model tiers. It is a bit long due to multiple pricing/use-case entries, but each item is actionable and the colon-separated format makes it scannable. No wasted words.

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 tool with 4 parameters and no output schema, the description covers purpose, limitations, and model selection thoroughly. It doesn't explain return values or error handling, but for an ask tool this is less critical. Auth/rate limits are not mentioned, but the context is sufficient for correct invocation.

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?

Schema coverage is 100% with good descriptions of all parameters. The description goes beyond the schema by enriching the 'model' parameter with concrete model names, pricing, and suitability (e.g., gpt-5.3-codex for code, nano for cheapest). This adds substantial value for model selection.

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 opens with 'Ask a single AI model a question' – a specific verb and resource. It explicitly states the scope (single model, no web search, no real-time info) and distinguishes from the research tool by directing multi-perspective questions elsewhere.

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?

Provides explicit when-to-use vs alternatives: 'For multiple perspectives on a question, prefer research tool with mode:ask synthesize:false.' It also maps model choices to use cases (code, mid-tier, cheaper, cheapest), giving clear guidance on when to select each.

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