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

With no annotations, the description carries the full behavioral burden. It implies a read-only ranking operation, which is useful, but says nothing about the schema's 50-row cap, what happens when a non-numeric column is passed, ordering ties, or the shape of the returned rows.

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 compact sentence that front-loads the operation and the dataset scope. The trailing parenthetical example is slightly cryptic and the sentence reads as a fragment, but there is no 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 top-N read tool with no output schema and no annotations, the description covers the core ranking behavior but omits the limit parameter, the 50-row maximum, and the return shape. Enough to call it roughly correctly, not enough to call it confidently.

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% (only `ascending` is documented in the schema). The description partially compensates by establishing that `column` selects the ranking key, that it must be numeric, and that direction is highest-by-default / lowest-on-request, but it entirely omits the `limit` parameter and its 1-50 bound.

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 gives a specific operation (return the highest/lowest rows of the Roofing Quotes UK dataset ranked by a numeric column) plus an intent framing ('which is the most/least X'). It is clear what the tool does, but never names or distinguishes itself from siblings like dataset_row or dataset_search, which also surface row data.

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?

There is no statement of when to use this tool versus dataset_row, dataset_stats, or dataset_search, no prerequisites (e.g. the column must be numeric), and no exclusions. The 'most/least X' phrasing hints at a ranking use case but gives no actionable routing guidance.

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