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

No annotations are supplied, so the description carries the full burden. It discloses that matching is case-insensitive and substring-based across any cell, which is genuinely useful behavior. It does not confirm return shape, ordering, default limit when 'limit' is omitted, or whether results are truncated silently at 50.

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 compact sentence that front-loads the resource and matching condition, then the cap. No filler, no redundant restatement of the tool name.

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, the description must account for returns; it says 'Rows of the ... dataset' but never sketches the row structure, which an agent must infer from sibling tools like dataset_columns. It is minimally sufficient for a 2-parameter, read-only lookup tool but leaves the response shape unspecified.

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 only 50% (the 'query' param is documented, 'limit' is not), so the description must compensate. It adds matching semantics ('case-insensitive', 'contain') and the 'up to 50' ceiling that clarifies the limit parameter, but it never states the default limit or that limit selects how many rows are returned.

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?

States a specific verb ('Rows ... whose cells contain the query') and resource ('the Miniature Paints Compare dataset'), with the matching scope (case-insensitive, substring in any cell) spelled out. This distinguishes it from sibling tools like dataset_row, dataset_top, and dataset_compare without naming them explicitly.

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 matching semantics: use it when you have a text query and want matching rows. It never names alternatives such as dataset_columns (for discovering fields) or dataset_row (for a single record), so no explicit when/when-not routing is provided.

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