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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses meaningful behavior: rows are matched when the column equals any of the given values, and results are returned in the order of the values provided. However, it does not mention output format, exact-match semantics, case sensitivity, handling of missing values, or explicitly confirm the operation is read-only.

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 with no filler. It front-loads the core behavior, includes the ordering detail, and adds a concise usage hint. Every phrase contributes meaning.

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 query tool, the description covers the core selection logic, ordering, and intended use case. The input schema supplies the remaining constraints such as min and max items. It does not describe the return structure, but the title and 'rows' language make the basic output shape reasonably inferable.

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 bare parameter names. It does so by explaining that 'column' is the dataset column to match against and 'values' are the values used for filtering and result ordering. This adds real semantic meaning beyond the schema, though it does not elaborate on edge cases like duplicate values or unmatched entries.

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 that the tool returns rows of the Extinvo dataset filtered by a specified column and set of values, in a given order, for comparison questions. It distinguishes itself from siblings like dataset_row and dataset_search through the multi-row 'X vs Y' framing, though it does not name any sibling explicitly.

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' provides clear contextual guidance on when to use this tool: when comparing specific rows side by side. It does not explicitly state when not to use it or name alternative tools, but the intended use case is clear enough to route an agent appropriately.

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