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 does disclose one real behavioral constraint — the result set is capped at 10 models — which is useful, but it says nothing about what is returned per match, whether matching is fuzzy, or how auth/rate limits apply.

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 leads with the action and then qualifies accepted inputs. No filler, though it packs three input modes and a limit into one dense clause.

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?

With no annotations and no output schema, the description should explain what a match looks like (deprecation date, replacement suggestion, status). It discloses the 10-result cap but not the return shape or matching semantics, leaving a gap for a tool whose output drives a migration decision.

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 0% and the single 'text' parameter is undocumented in the schema, so the description must compensate — and it does, telling the agent the parameter accepts pasted code, config files, or a list of model IDs. That is meaningful beyond the bare 'string' type, though it could still clarify list formatting.

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 (find deprecated/about-to-be-deprecated models) and names the accepted inputs (code, config files, model ID list). It covers both the 'already shut down' and 'upcoming' cases, which makes it hard to distinguish from the sibling upcoming_ai_model_shutdowns, so it stops short of a 5.

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 the input types (paste code/config for a bulk scan), but there is no explicit when-to-use guidance or naming of alternatives such as upcoming_ai_model_shutdowns or find_ai_model_replacement. An agent can infer the scenario but is not routed to the right sibling.

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