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

With no annotations provided, the description carries the full disclosure burden, but it only conveys the core ordering behavior (highest or lowest by numeric column). It does not address tie handling, null values, behavior for non-numeric columns, or whether full rows or single values are returned. The 'ascending' default is only visible in the schema, not the description.

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 compact and the core operation is front-loaded. However, the sentence is a fragment and the embedded 'Taxyearvo' dataset reference reads as environment-specific hardcoding that may confuse an agent. The quoted use case at the end is a worthwhile addition that 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 tool with no annotations and no output schema, the description omits the return shape (full rows vs. values only) and failure behavior for non-numeric columns. The required parameter is adequately constrained ('numeric column'), so an agent can make a first call, but nothing explains defaults or edge cases. A sentence on return format and ordering defaults would complete it.

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 should compensate. It adds one useful constraint — the column must be numeric — but says nothing about the 'limit' parameter, whose semantics are left to inference from its name and schema bounds. The 'ascending' parameter is already adequately documented by the schema's own description.

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 concrete operation: returning the highest or lowest rows of a dataset by a numeric column, framed as 'which is the most/least X.' This distinguishes it from siblings like dataset_stats (aggregation), dataset_row (single row), and dataset_search (matching). Clarity is dented by the hardcoded 'Taxyearvo' dataset name and a sentence-fragment construction with no main verb.

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 description offers no guidance on when to choose this tool over sibling tools such as dataset_row, dataset_stats, or dataset_search. The quoted use case ('which is the most/least X') is essentially a restatement of purpose, not a selection rule. No prerequisites, exclusions, or alternative conditions are given.

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