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

With no annotations provided, the description carries the behavioral disclosure burden. It does meaningful work by specifying filter semantics ('any of the given values'), ordering behavior ('in the order given'), and that whole rows are returned. It does not mention edge cases such as no matches or duplicate values, but it gives enough behavioral context for a query tool.

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, dense sentence that packs in the target resource, the filtering logic, ordering behavior, and the intended use case. The title adds the 'side by side comparison' framing. There is no redundancy or filler.

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 tool, the description covers what is selected, how filtering works, and how ordering is determined. Since there is no output schema, it would be helpful to state the exact shape of returned rows or behavior on empty results, but an agent can still invoke the tool correctly for comparison 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 for the schema's lack of semantic detail. It does this well: 'column' is identified as the attribute to match, 'values' as the list of allowed values, and 'any of' plus 'in the order given' define matching and ordering semantics. This adds substantial meaning beyond the raw input schema.

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 operation: return rows of the Jobcardo dataset where the column matches any of the given values, preserving the given order. It also conveys the intended use case ('for "X vs Y" questions'), which makes the tool's purpose easy to grasp. It does not explicitly distinguish itself from sibling tools like dataset_search or dataset_row, so it is clear but not fully differentiated.

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' signals when this tool is appropriate, giving an agent contextual guidance beyond a bare functional description. However, it does not name alternative sibling tools or state explicit when-not-to-use conditions, so the guidance is clear but incomplete.

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