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

No annotations are provided, so the description carries the full behavioral burden, and it discloses little beyond the ordering guarantee. It does not say whether the tool is read-only (implied), what happens when a value matches nothing, how rows are shaped, or that at most 10 values are accepted.

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 tight sentence with no filler, and the comparison use case is placed at the end as a clear qualifier. It is efficient, though the leading fragment construction makes it slightly less immediately scannable than a verb-first statement.

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 tool with no annotations, no output schema, and zero schema description coverage, the description is too thin: return shape, no-match behavior, and how 'column' relates to the sibling dataset_columns tool are all left unstated.

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 does explain the matching semantics (column value is any of the given values) and that result order follows the input order, which is real added meaning. It still omits the 2–10 item bounds and what 'column' can accept (e.g. valid column names, or whether dataset_columns should be consulted first).

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 what is returned (rows of the Lettza dataset matching the given column values, in the given order) and hints at the comparison use case, but it is phrased as a noun fragment rather than a clear verb+resource, and it never explicitly says this compares rows. Sibling tools dataset_row and dataset_search also return rows, so the distinction from them is only weakly drawn.

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 trailing phrase 'for "X vs Y" questions' implies the usage context, which is genuinely helpful. However, there is no explicit when-to-use vs when-not, and no named alternative (e.g. dataset_row or dataset_search) for single-row or free-text lookups.

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