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 usefully reveals that row ordering follows the order of the provided values, which is a meaningful behavioral detail. It does not mention exact-match semantics, case sensitivity, or the shape of the output, so the agent still has some uncertainty about the tool's behavior.

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, compact sentence that packs in the dataset name, the filter logic, the ordering behavior, and the intended use case. Every part earns its place, with 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 tool, the description covers the core selection logic and ordering, but it omits output format and does not explain how this relates to the 'side by side' title. It also lacks explicit guidance distinguishing it from dataset_row or dataset_search, which could be important for correct tool selection.

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 to both parameters. It effectively explains that 'column' is the field to match and 'values' are the candidate values to filter rows by, and that their order determines result order. This compensates well for the missing schema descriptions, though it does not explicitly name the parameters or their constraints.

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's function: retrieving rows from the Fitouto dataset where a specified column matches any of the given values, preserving the given order. It also ties this to 'X vs Y' questions, which differentiates it from broader sibling tools like dataset_search or dataset_stats. However, it lacks an explicit action verb like 'return' or 'select', making the phrasing slightly indirect.

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 use case ('for X vs Y questions'), implying when it should be used. It does not explicitly contrast with sibling tools or state when not to use this tool, leaving some inference needed.

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