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

No annotations are present, so the description carries full responsibility. It mentions the filtering and ordering behavior but omits side effects, error conditions (e.g., unknown column, missing values), and read-only guarantees. This is a significant gap for a data-access 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?

A single concise sentence captures the core behavior and purpose without unnecessary words. The structure is front-loaded with the primary action and then supported by the ordering detail and intended use case.

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 simple parameters and no output schema, the description is sufficiently complete for typical use. It covers filtering, ordering, and the comparison use case. It could mention the limit of 10 values, but that is already enforced in the schema, so it is not a critical omission.

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?

The description explains that 'column' selects the attribute to match and 'values' are the target values, which gives partial semantics. However, it does not clarify whether column is a name or index, or what value types are expected. With 0% schema coverage, more detail would be beneficial.

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 compares rows side-by-side by filtering a specified column for any of the given values, preserving the provided order. This distinguishes it from siblings like dataset_row (single row) and dataset_search (general search).

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' gives a clear use case, but it does not explicitly contrast with alternatives (e.g., when to use dataset_search or dataset_row instead). The guidance is implied through purpose but lacks direct exclusions.

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