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

With no annotations, the description bears full responsibility for behavioral disclosure. It reveals that rows are returned in the order of given values, but omits critical details such as whether the output is a list, how missing values are handled, case sensitivity, pagination, or error behavior. The agent has insufficient information to predict the tool's exact behavior beyond the basic filter.

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, concise sentence that front-loads the core purpose and key constraint (order). It wastes no words and communicates the primary function effectively, though it could benefit from a slightly more structured breakdown of parameters and outputs.

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 absence of an output schema, annotations, and any schema property descriptions, the tool definition is incomplete. The description does not explain the return format, potential errors, or the exact expectations for 'column' and 'values' (e.g., whether column names are case-sensitive, whether values need to match exactly). An agent would struggle to confidently construct a valid call without external knowledge of the dataset.

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 coverage is 0%, so the description must compensate. It clarifies that 'column' is the field to filter on and 'values' are the values to match, and notes that row order follows the given value order. However, it does not explain that 'column' must be a valid column name from the dataset, nor does it specify constraints like case sensitivity or matching logic (exact vs substring). The description adds value but leaves several semantic gaps.

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 a specific action (retrieve rows) on a specific resource (Card Machine Pricing dataset) with a defined filter condition (column matches any given values) and ordering behavior. It explicitly ties to a use case ('X vs Y' questions), making it distinguishable from sibling tools like dataset_row (single row) or dataset_search (keyword 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 phrase 'for X vs Y questions' implies a comparative use case, but it does not explicitly state when to use this tool over alternatives or when not to use it. No sibling tools are referenced, and there is no guidance on prerequisites such as valid column names or value formats. The intended context is only partially conveyed.

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