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

With no annotations, the description carries the full burden of disclosure. It conveys the core behavior (sort by a numeric column and return top/bottom rows) but omits important details such as the default limit, default ascending direction, tie-handling, output shape, and behavior on non-numeric columns.

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 focused sentence with a useful usage example. It is front-loaded with the core operation and contains no filler, making it efficient and easy to parse.

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 tool with no output schema and no annotations, the description should state what the result contains and the default behavior. It only says 'rows' without specifying output format, default limit, or ordering defaults, so an agent could call it with incomplete expectations.

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?

Only one of three parameters has a schema description, so the description must compensate. It adds meaning for 'column' as numeric and for 'ascending' via highest/lowest, but it does not clarify the 'limit' parameter or its default, leaving a notable gap.

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 states a specific verb and resource: returning 'the highest (or lowest) rows' of the dataset by a numeric column, reinforced by the title 'Rank rows by a numeric column'. This clearly differentiates it from siblings like dataset_search or dataset_stats, though it does not name them explicitly.

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 'which is the most/least X' provides a practical query template, implying when the tool is useful. However, it offers no explicit guidance on when to prefer this over sibling tools or any exclusions, leaving the decision partly to inference.

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