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

With no annotations, the description carries the full behavioral burden. It does disclose the core behavior—returning ranked rows by a numeric column—and implies a read-only query. It does not mention default ordering, handling of non-numeric columns, tie behavior, or output structure, leaving partial transparency.

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 one front-loaded sentence with a useful query pattern and no wasted words. It avoids repeating schema details and conveys the essential intent efficiently.

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 three-parameter tool, the description plus schema is largely sufficient for selecting and invoking it. The absence of an output schema and annotations makes the lack of explicit return-format, default-limit, and read-only confirmation a noticeable gap, but not a blocking one.

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 only 33%, so the description must compensate. It adds meaning by specifying that the column must be numeric and that the result is ordered highest or lowest, which maps to the ascending parameter. The limit parameter is not explained in the description, though its name and schema constraints make it partly self-evident.

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 identifies the operation: returning the highest or lowest rows of the dataset by a numeric column, framed as 'which is the most/least X'. This distinguishes it from siblings like dataset_stats or dataset_row, though it does not explicitly name a contrasting sibling.

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 'which is the most/least X' phrasing implies the intended use case, and the focus on rows by numeric column separates it from column/stats/search tools. However, there is no explicit when-not-to-use guidance or named alternative, so the usage guidance is mostly inferred rather than stated.

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