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

No annotations are provided, so the description carries the behavioral burden. It discloses that the result is text and that an external catalog model is consulted, but it does not mention potential costs, balance consumption, latency, errors, or other side effects despite the sibling tool balance hinting at monetary implications.

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 core purpose is front-loaded and expressed in one clear sentence, with a brief context sentence afterward. The Russian translation adds redundant length, but the overall description is short and scannable.

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 moderately simple tool with fully documented parameters, the description covers what the tool does, what it returns, and when it is useful. Some behavioral details like cost or failure modes are absent, but the schema plus this description are 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 description coverage is 100%, so the schema already documents model, prompt, system, and max_tokens. The description adds minimal parameter meaning beyond naming model families and the text response, so it stays at the baseline rather than adding substantial value.

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'), identifies the resource ('any model from the Store API catalog'), and states the output ('answer as text'). It also mentions distinct model families and the 'second model' use case, which separates it from siblings like list_models, generate_image, and balance.

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 gives clear context: use this when you want to consult a second model from inside the current chat. It does not explicitly name alternatives or exclusions, but the intended use case is evident and the schema points to list_models for obtaining model slugs.

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