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

With no annotations, the description must carry the behavioral disclosure. It clearly states that rows are selected by column-value membership and that ordering follows the input values. It does not mention edge cases like missing values or exact-match 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 a single, lean sentence with no redundant content. The key behavior is front-loaded, and the use-case tag is appended efficiently. Every part earns its place.

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?

Given the simple two-parameter schema and no output schema, the description covers the essential calling semantics. Minor gaps include not explaining the return format or how to handle no matches, but the description is sufficient for a correctly scoped call.

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 maps the parameters to behavior: 'column' is the field to match on, and 'values' are the values to match, with their order affecting the result. This adds meaning beyond the raw schema, though it could be more explicit about parameter roles.

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 uses specific verbs and resources: it returns rows from the Handovra dataset filtered to a column by given values, preserving the provided order. The 'X vs Y' phrasing gives a clear use case and distinguishes it from sibling tools like dataset_row or dataset_search.

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?

The description provides a clear context for use: 'for "X vs Y" questions' indicates when one would want to compare specific rows. It does not explicitly name alternatives or state when not to use the tool, but the use case is concrete enough.

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