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

With no annotations provided, the description carries the full behavioral burden, and it does a good job: it discloses that rows are filtered by 'any of the given values' and that output order follows the supplied value order. It does not mention matching semantics such as exact/case-sensitive matching, but the core behavior is transparent for a straightforward read operation.

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 sentence with no filler, and the most important behavioral facts are front-loaded: dataset, filter by column, any-of values, and order preservation. 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 the simplicity of the operation, two required parameters, and no output schema, the description covers the essential selection and ordering behavior adequately. It could add edge-case context such as what happens when no rows match, but nothing critical is missing for a basic agent 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?

The input schema has 0% description coverage, but the description compensates by explaining both parameters in plain language: 'column' is the field to match, and 'values' is the list whose order determines result order. This meaningfully goes beyond the bare JSON Schema, though it could more explicitly name the parameters and define value format constraints.

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 identifies the target dataset and the selection mechanism: rows whose column value matches any of the supplied values, in the given order. Combined with the title 'Compare rows side by side', the core purpose is clear enough for an agent to understand the operation. It does not explicitly name a sibling tool it is distinct from, so it doesn't quite reach the top score.

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' gives a clear, concrete use context, telling the agent when this tool is appropriate. It does not explicitly exclude alternatives or name sibling tools such as dataset_row or dataset_search, so it stops short of full routing guidance.

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