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

No annotations are provided, so the description carries the full burden. It implies a read-only lookup but discloses nothing about how the query is matched (model id vs. display name), what happens when no replacement exists, or what the response contains. For a tool with zero annotation coverage this is thin.

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 plus a category prefix; nothing is redundant or padded. It is efficiently sized for the tool's scope, though extremely terse.

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 with no output schema, the description is minimally adequate: it tells the agent what it returns conceptually. However it omits the query format and the semantics of the result, which the absent structured fields would otherwise cover.

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 has 0% schema coverage (bare type: string). The description only implies q is "a certain model," adding slight meaning but no format guidance (e.g., model identifier or name). This is insufficient for a keyword-driven lookup.

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 gives a specific verb+resource: look up which model an official recommendation suggests as a replacement. The bracketed category tag ("[AI 模型下架與替代日程]") reinforces the domain. It is understandable without opening the schema, though it doesn't explicitly name or contrast with siblings like upcoming_ai_model_shutdowns or ai_model_shutdown_calendar.

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?

No when-to-use, when-not-to-use, or alternative routing is provided. The agent must infer that this is for finding replacements versus the shutdown calendar or deprecation-check siblings. Usage is only implied by the tool's subject matter.

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