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

With no annotations present, the description must carry the full disclosure burden, and it discloses little. It does not say what happens for an unknown or unreleased model (empty result vs. error), how fresh the shutdown data is, or whether it is a pure read. The mention that the result includes a 'source' is the one real disclosure, but it is thin for a tool with zero annotation coverage.

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 names the resource, the returned fields, and example inputs, with no wasted words. The most important information, what the tool returns, comes first.

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 two-parameter read with no output schema and no annotations, the description does cover the return shape (date, replacement, source), which is valuable. It nonetheless omits the not-found behavior and the provider param semantics, leaving real gaps given that no structured field fills them.

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 50%: the provider param documented but the required model param is not. The description partly compensates by showing example model IDs (gpt-4o-2024-08-06, claude-sonnet-4-5), which conveys the expected format. It adds nothing about the provider param's accepted values or effect, so the gap is only half closed.

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 states a specific verb and resource ('Get the official deprecation/shutdown date') and even names the returned fields (date, replacement, source), so the agent knows exactly what comes back. It gives concrete model-ID examples in two naming conventions. However, it never names a sibling tool such as list_upcoming or search_models, so differentiation is left to the agent's inference.

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?

There is no explicit guidance on when to use this instead of list_upcoming or search_models. The phrase 'for an AI model ID' implies single-model lookup, and the alternative — using list_upcoming for a bulk view — is never stated, nor is there any guidance on what to do when a model is not found.

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