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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal key behavior: it filters rows where the column matches any of the given values and returns them in the order of the values. However, it omits edge cases like case sensitivity, exact-match semantics, handling of missing values, duplicates, or any limits. This is a moderate disclosure but leaves significant gaps.

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 behavior and includes a usage hint. There is no fluff or repetition; every word adds value. It is well-structured and efficient.

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 tool's simplicity (2 params, no output schema, no annotations), the description covers the essential behavior: filtering and ordering. However, it lacks details about the output format (e.g., whether full rows are returned), error handling (e.g., no matches), and exact matching rules. These gaps are not severe but an agent might need additional information to call it confidently in all scenarios.

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 explains the role of 'column' (the field to filter on) and 'values' (the list of values to match) in context, and adds the ordering behavior. However, it does not clarify whether matches are exact or case-sensitive, or that values are a list (though the schema defines array type). The description adds meaning but not comprehensive detail.

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 the tool retrieves rows from a specific dataset (Rollupvo) filtered by a column matching any of the given values, preserving the order of the values. The verb is implicit (retrieve/filter) but the resource and action are clear. It does not explicitly differentiate from siblings like dataset_row or dataset_search, but the 'X vs Y' hint gives context that distinguishes it from generic retrieval.

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 hints at usage with 'for "X vs Y" questions,' implying it is for comparing specific values. However, it does not explicitly state when not to use it, mention alternative tools, or describe scenarios where another sibling would be preferred. The guidance is implied rather than explicit.

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