Search curated screenshot patterns

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 explicitly discloses that results are human-reviewed, include text-match reasons, and are references rather than conversion evidence. These are meaningful behavioral traits beyond what the schema shows. It does not cover ordering, pagination, or filter semantics, but the core behavior is transparent.

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 two sentences: the first front-loads the purpose and facets, the second frames result interpretation. Every clause earns its place, with no redundancy or filler.

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 9-parameter tool with no output schema and no annotations, the description covers the high-level purpose but leaves gaps: it does not explain multi-filter logic, what constitutes a valid query or array value, defaults (e.g., limit=8, per_app_limit=2), or the meaning of exclude_slugs. An agent could still invoke it correctly with a simple query, but the description is not fully complete for effective use of all parameters.

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 only 44%, so the description must compensate. It merely re-lists the same facet names as parameters (communication job, visual style, composition, device treatment, category) without adding value. It does not explain what values are valid, whether filters combine as AND/OR, or what 'human-reviewed' means for query behavior. The small benefit of indicating curated data does not offset the missing guidance.

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: 'Search human-reviewed screenshot-level annotations' and lists six distinct facets. It clearly differentiates from sibling tools like search_apps by scoping to screenshot-level annotations, and the second sentence clarifies the intended use case (references, not conversion evidence).

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 purpose: an agent can infer this tool is for finding curated screenshot patterns by various facets. However, there is no explicit when-to-use versus alternatives, no mention of when not to use it (e.g., when needing app metadata), and no comparison to search_apps or get_reference_board. The guidance is present but not stated.

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