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

With no annotations and no output schema, the description carries the full behavioral disclosure burden. It does reveal one useful trait ('in the order given'), but it omits important behavior: whether matching is exact, what happens with no matches, how results are returned, and what 'side by side' means structurally. This is thin for a tool with no annotation safety net.

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 that front-loads the core behavior and includes the key ordering detail. It is concise and free of filler. The only minor issue is slightly awkward wording ('whose column is any of the given values'), but it remains 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, the description conveys the basic idea but leaves essential gaps: it never says how 'column' relates to the dataset schema, what valid value formats are, or what the output structure looks like. Without an output schema or annotations, an agent would need to guess or inspect sibling tools like dataset_columns to use this correctly.

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 for the undocumented parameters. It mentions 'column' and 'given values,' but does not explain that 'column' is the dataset field to match on or that 'values' are the exact cell values to filter by. It also does not clarify constraints like case sensitivity or value format. This is insufficient compensation for the schema gap.

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 states a specific action (retrieve rows) and resource (Focusvo dataset), with a clear scope: rows whose column value matches any of the given values, preserving the given value order. The phrase 'for "X vs Y" questions' and the title 'Compare rows side by side' help differentiate it from sibling tools like dataset_row (single row) and dataset_search (general search), though it does not explicitly name them.

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 usage context: use for 'X vs Y' questions where rows need to be compared side by side. It does not explicitly state when not to use it or name alternative tools, but the narrow comparison purpose is a sufficiently clear guideline.

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