search_random

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

With no annotations provided, the description carries the full burden and clearly discloses 'Read-only' along with the JSON return format. It also flags the OCR dependency ('needs OCR on the server') and that results are random, which are non-obvious behavioral traits.

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 well-organized: purpose and usage up front, then an Args block, then Returns. Each sentence contributes necessary information, and nothing is redundant or wasteful given the 7-parameter surface.

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 read-only random-search tool with no annotations and 7 optional parameters, the description covers purpose, use cases, parameter semantics, safety (read-only), and return shape. The output schema provides the formal structure, so the description fully equips an agent to invoke it correctly.

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 fully compensate, and it does. Every parameter (size, city, country, make, model, is_favorite, ocr) gets a plain-language meaning, with size's default/max and the OCR server dependency highlighted.

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?

Description opens with 'Random assets from the library, optionally filtered' – a specific verb, resource, and mode that sets it apart from all other search_* siblings. The sampling use cases ('quick feel', 'surprise pick', 'spot checks') further clarify its unique role without confusion.

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?

Explicitly states when to use the tool with concrete use cases like sampling and spot checks. It does not name alternative tools or give exclusion criteria, but the context is clear enough that an agent can decide this is the random/sampling choice among the many search tools.

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