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

Annotations already declare the tool read-only and non-destructive, so the description only needs to add behavioral context. It adds the search scope across model_name and description, but does not disclose matching semantics, case sensitivity, ordering, or pagination behavior.

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 a single sentence with no filler, and the key scoping information (which fields are searched) is front-loaded. It is terse but appropriately sized for a simple search operation.

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 an annotated read-only search tool with an output schema, the description is minimally viable but leaves gaps: it does not mention result ordering, limit behavior, or how this search differs from related search tools. It is adequate but not comprehensive.

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%, so the description must compensate. It implies that the q parameter is the keyword applied to model_name + description, which adds meaning, but it never explicitly names q or explains the limit parameter beyond what the schema constraints provide.

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 action (keyword search), the resource (models), and the specific fields searched (model_name + description). This is specific enough to distinguish it from list/lookup operations, though it does not explicitly reference sibling tools.

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 guidance is given about when to use this versus models.list, models.lookup, or theoretical_models.search. The keyword-search intent is implied by the tool name and one-line description, but there are no explicit conditions, exclusions, or alternative recommendations.

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