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

With no annotations, the description must carry behavioral disclosure. It states that matching rows are returned for any of the given values and that result order follows the given values, which is useful. It does not explain match semantics (exactness/case sensitivity), missing-value behavior, or response shape, leaving meaningful gaps for a read tool without an output schema.

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 definition is a single compact sentence with no wasted wording, and the dataset resource plus comparison use case are front-loaded. The sentence is somewhat fragmentary and could more directly state that it returns matching rows, but it is appropriately sized.

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 simple two-parameter read/compare tool with no annotations or output schema, the description covers the core data source, filter, ordering, and use case. It still leaves important gaps around parameter constraints, match semantics, and response behavior, so it is only adequately 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?

Schema description coverage is 0%, so the description should compensate. It does clarify that "column" selects the field to match and "values" supplies the comparison values whose order is preserved, but it omits the schema constraints (at least 2 and at most 10 values) and does not explain exact matching or duplicate handling.

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 identifies a specific resource (rows of the Card Machine Pricing dataset) and a specific filter operation (column values in the order given), and frames it for "X vs Y" questions. It is clear enough to distinguish from generic search/listing tools, though it lacks an explicit verb and does not name sibling alternatives.

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 clear intended usage and implies comparison of 2–10 specified values. However, it provides no explicit when-not guidance or named alternatives such as dataset_row for a single row or dataset_search for broader querying.

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