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

With no annotations, the description carries the behavioral burden. It discloses key behavior—filtering by 'any of the given values' and returning rows 'in the order given'—but it does not mention exact-match semantics, behavior on no matches, or the output row shape, especially since no output schema exists.

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?

A single, front-loaded sentence states the result, the filtering rule, the ordering behavior, and the intended use case. There is no filler or redundancy.

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, the description provides enough to invoke it: what rows are returned, how they are filtered, and how they are ordered. Remaining gaps such as no-match behavior and output shape are minor, and valid columns can be discovered via the sibling dataset_columns tool.

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 add meaning. It links both parameters: 'column' is the field to filter on and 'values' are the values to match, and it adds the crucial detail that the values array order controls row order. It does not specify valid column names or value formatting, but the core semantics are covered.

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 operation: return rows from the Dividvo dataset where the given column matches any of the supplied values, preserving the supplied value order. The title 'Compare rows side by side' and the 'for X vs Y questions' tag distinguish it from sibling tools like dataset_search and dataset_row.

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 gives a clear intended use case ('for X vs Y questions') and implies this tool is for comparing specific column values rather than searching or aggregating. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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