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

With no annotations, the description carries the behavioral burden. It discloses the core read-only behavior of selecting highest or lowest rows by a numeric column, but it does not mention output shape, tie/null handling, invalid column behavior, default ordering, or limit defaults. This is minimally viable but leaves gaps.

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 efficient sentence with a clarifying user-facing question. Every part earns its place, and the core operation is front-loaded without filler.

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 ranking tool, the description is nearly adequate: the operation is clear and the optional parameters are expressed in the schema. But without an output schema or annotations, the missing default-limit behavior and return shape leave an agent guessing on important details.

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 usefully clarifies that column must be numeric and connects ascending to highest/lowest, but it never mentions limit, its default, or its bounds. The schema's min/max partially covers limit, but only barely.

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 operation: returning the highest or lowest rows of the dataset ranked by a numeric column, with the natural-language gloss 'which is the most/least X'. This clearly differentiates it from siblings like dataset_stats, dataset_row, and dataset_search, 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 'which is the most/least X' gloss implies the intended use case for top/bottom-N ranking questions, and the contrast with stats/search/row tools is implicit. However, the description gives no explicit guidance about when to prefer this tool over a sibling or when not to use it.

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