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

There are no annotations, so the description carries the behavioral burden. It usefully adds the 'FREE' and 'fast' traits and specifies the return content (callability, sunset date, days remaining). However, it does not disclose behavior for invalid/expired model IDs, response shape, or potential error conditions, which leaves some transparency gaps.

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 three short sentences with no filler. It front-loads the most decision-relevant facts: cost, purpose, and when to use it.

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 tool, the description gives enough context to recognize when to use it and what it returns. However, with no output schema and no annotations, it does not explain edge cases like unknown model IDs, and the exact response format is left unspecified.

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 0% and the description never explicitly references model_id or explains how to provide it. The single parameter name is self-explanatory, which prevents a score of 1, but the description does not add meaningful semantic value beyond the schema.

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 states the operation: checking whether an AI model is still callable, and it adds the deliverable (sunset date and days remaining). The verb 'check' plus the resource 'model' makes the purpose clear. It does not explicitly differentiate from siblings such as model_lifecycle or get_model_lifecycle_all, but the tool's niche is evident from the description.

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?

'Use before depending on a model at runtime' gives a concrete, decision-oriented usage context, so an agent knows when this check is appropriate. It does not name alternatives or explicitly spell out when not to use it, but the provided context is clear and actionable.

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