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

No annotations are provided, so the description carries the burden. It usefully discloses what information is returned (status, dates, replacement, source), but never states that this is a read-only lookup or describes error behavior for unknown IDs.

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?

A single front-loaded sentence that packs the resource, examples, and returned fields with no filler. Dense but not padded.

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 lookup this is largely sufficient — the returned fields are enumerated even without an output schema. The main gap is the absence of routing guidance against the five sibling tools, which all touch the same model data.

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?

With only one parameter and 0% schema description coverage, the description compensates well by naming it ('模型 ID') and giving three concrete format examples (gpt-4o-2024-05-13, claude-3-5-haiku-20241022, gemini-2.0-flash) that show the expected identifier shape.

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?

States a specific verb+resource (查單一模型 ID) and enumerates the fields returned: status, retirement date, shutdown date, days remaining, official replacement, source. This clearly distinguishes it from the plural list_ai_models, though it never explicitly names a sibling.

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?

Usage is implied by '單一模型 ID' (one model, not a list or calendar), which contrasts implicitly with list_ai_models and ai_model_shutdown_calendar. However there is no explicit statement of when to use this versus find_ai_model_replacement or upcoming_ai_model_shutdowns.

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