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

No annotations are provided, so the description must carry the behavioral burden. It discloses the ordering behavior (highest/lowest) but omits important traits such as how ties are handled, whether non-numeric columns cause errors, what the return payload contains, and whether the result is simply a filtered list or includes ranking metadata.

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, front-loaded sentence that conveys the core operation and the natural-language intent without redundancy. Every word earns its place.

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 ranking tool with no output schema and no annotations, the description gives enough to understand the basic operation, but it leaves gaps: no mention of limit semantics, default behavior, output shape, or edge cases. An agent could invoke it correctly but might misjudge boundary behavior or result format.

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 only 33%, so the description must compensate. It adds the key constraint that the column must be numeric, which is genuinely useful. However, it does not explain the limit parameter's role or the ascending default beyond what the schema's brief 'ascending' description states.

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 clear verb-resource relationship: it returns the highest or lowest rows of the dataset based on a numeric column, with the natural-language framing 'which is the most/least X'. This distinguishes it from row-fetch, search, stats, and column tools, though sibling differentiation is not explicit.

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 intended use case is implied clearly: use when you need the top or bottom rows ranked by a numeric column. However, there is no explicit guidance about when to prefer this over siblings like dataset_stats or dataset_search, and no exclusionary conditions.

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