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

Annotations already declare the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lowered. The description adds value beyond them by disclosing what each result contains — cheapest standard price per 1M tokens, seller, seller count, and page link — which is behavioral output context the annotations do not carry.

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?

Three sentences, front-loaded with the search axes, followed by the result shape and a practical tip. No sentence is redundant and nothing is buried.

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 output schema available, the description compensates by describing the returned fields, and the input filters are fully covered by the schema. An agent has everything needed to call it and interpret results.

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 100%, so the baseline is 3. The description earns an extra point by describing the interaction between two parameters (sort='price' combined with min_score), which the schema documents only in isolation.

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 (Find) and resource (LLM API models) and enumerates the axes it searches on (name, developer, min score, max input price), which is far more specific than the title alone. It does not, however, explicitly differentiate itself from the closest sibling get_model_prices, leaving that inference to the reader.

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?

It gives concrete usage guidance for a real scenario: use sort='price' with min_score to find the cheapest model that is good enough. This is a clear when-to-use hint, but there is no when-not guidance and no explicit pointer to the alternative sibling tools.

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