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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the key behavior: filtering rows by a column matching any of the given values, and preserving the order of the values. However, it does not mention error handling (e.g., missing column), return format details, or potential side effects (though likely read-only). The description is functional but lacks depth beyond the core filtering and ordering.

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, front-loaded sentence that efficiently states the core operation and the intended use case. There is no redundant information, and every word earns its place. It is well-structured for quick comprehension.

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 tool's simplicity (2 params, no output schema, no annotations), the description covers the main functionality but leaves gaps. It does not explain the return format (though the title hints at 'side by side'), nor does it address error conditions or edge cases (e.g., what happens if a value is not found). For a tool with no output schema, more detail on what the agent should expect in the response would be helpful, but the core filtering and ordering are specified.

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 clarify the parameters. It implicitly explains 'column' as a column of the dataset and 'values' as the list of values to match, and that the order of values matters. This adds meaning beyond the raw schema, but it could be more explicit about what 'column' refers to (e.g., a column name) and whether matching is exact or case-sensitive. It partially compensates for the lack of schema descriptions.

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 it returns rows from the Ppmly dataset filtered by a column matching any given values, in the provided order. It distinguishes itself from siblings like dataset_row (which likely fetches a single row) by indicating a multi-row comparison use case ('for X vs Y questions'). However, it doesn't explicitly name a sibling or contrast the tool's specific role beyond the title.

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 implies usage context with 'for X vs Y questions', which is a clear use case. But it does not explicitly state when to use this tool instead of alternatives like dataset_search or dataset_row, nor does it mention when not to use it. The guidance is implied rather than explicit.

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