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

With no annotations, the description carries the full behavioral burden but discloses little: it does not say whether results are ordered, how ties are broken, what the default limit is, whether limit caps the dataset scan, or what the return shape looks like. Only the core ranking behavior is conveyed.

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 efficient sentence with no waste, and the core behavior (ranking by a numeric column) is front-loaded. The em-dash gloss aids comprehension without padding.

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 with no output schema, the description covers purpose but omits limit semantics, ordering/return behavior, and default behavior for the one required column argument. Adequate but with clear gaps for correct invocation.

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% (only 'ascending' is documented), so the description must compensate. It adds one useful constraint beyond the schema — that the target column must be numeric — but says nothing about 'limit' (bounds, default, effect), so the gap is only partly closed.

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?

States a specific verb+resource: ranking the highest/lowest rows of the Send A Parcel Compare dataset by a numeric column, and renders the intent in natural language ('which is the most/least X'). It is clearly distinguishable from siblings like dataset_stats or dataset_search. It stops short of explicitly naming which sibling to use when, but the purpose itself is unambiguous.

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' implies the query shape this tool answers, so usage is inferable, but there is no explicit when-to-use vs alternatives (e.g., versus dataset_search or dataset_stats) and no stated preconditions. It lands at implied-usage level.

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