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

There are no annotations, so the description carries the full burden. It discloses the row-selection logic and ordering semantics, which is valuable. However, it does not describe the output layout (despite the title saying 'side by side'), whether all matching rows are returned, or how unmatched values are handled.

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 sentence with no filler. It front-loads the core behavior and ends with the intended use case. Every clause 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 only 2 simple parameters and no output schema, the description conveys the essential return value: rows in the specified order. It does not describe the precise rendering of the side-by-side comparison, but for a lightweight retrieval tool this is a minor gap.

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: 'column' is explained as the field to match against, and 'values' is explained as the list that determines both membership and output order. The minimum/maximum constraints are left to the schema, but the core semantics are clear.

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 the operation in concrete terms: it returns rows whose column matches any given values, in the order given. It also distinguishes itself from sibling tools by explicitly targeting 'X vs Y' comparison scenarios, so an agent can tell it apart from 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 clearly signals when to use the tool ('for X vs Y questions') and implies the intended comparison use case. It does not explicitly name alternatives or exclusion conditions, but the context is unambiguous enough for a capable agent.

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