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

No annotations are provided, so the description carries the full burden. It discloses the selection behavior and the order-preservation behavior, which is useful. However, it does not describe the output format, whether all columns are returned, or what happens when no rows match, leaving some ambiguity in the exact behavior.

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, well-structured sentence that includes the target dataset, the filtering logic, the ordering behavior, and the intended use case. Every part earns its place with no unnecessary words.

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 tool with no output schema, the description covers the core invocation details: which dataset, which column, which values, and the output order. It does not specify the exact return representation, but the phrase 'The rows' gives a reasonable expectation that full row data is returned.

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?

With schema description coverage at 0%, the description compensates by explaining that 'column' is a column in the dataset and 'values' are the values to match. It adds the important semantic that the order of the values array determines the output row order, which is not apparent from the schema alone.

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 operation: it returns rows from the OrderPadLedger dataset filtered by a column matching any of the supplied values, in the order given. It distinguishes itself from sibling tools like dataset_row and dataset_search by framing the use case as 'X vs Y' comparisons. The missing explicit verb is a minor flaw, but the intent is unambiguous.

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 provides clear context for when to use this tool — for 'X vs Y' questions where specific values of a column need to be compared side by side. It does not explicitly name alternatives or state when not to use it, but the differentiation from dataset_search, dataset_row, and dataset_stats is implied by the focused comparison use case.

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