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

No annotations are provided, and the description does not disclose any behavioral traits such as side effects, permissions, rate limits, or whether the operation is read-only. The description carries full burden but fails to address these aspects.

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, succinct sentence that directly conveys the tool's functionality without any unnecessary words or filler. It is well-structured and easy to parse.

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 tool's simplicity and the absence of an output schema, the description provides sufficient context for basic usage. Minor details like behavior when no rows match or exact output format are omitted but can be inferred, so it's mostly complete.

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?

The description explains that 'column' is used to filter rows and 'values' are the set of values to match, adding meaning beyond the schema's basic types and constraints. It also clarifies that the result order follows the order of 'values', which is not explicit in the schema.

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 clearly states the tool retrieves rows from the dataset based on a column matching one of the given values, with explicit ordering. It differentiates from sibling tools like dataset_row (single row) and dataset_search (query-based) by specifying side-by-side comparison.

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 phrase 'for "X vs Y" questions' explicitly indicates when to use this tool, providing clear usage context. However, it does not mention when not to use it or directly name alternative tools, so it falls short of fully explicit guidance.

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