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

With no annotations at all, the description carries the full behavioral burden. It does disclose two real traits: the dataset is fixed to Payroll Services Quotes, and result order follows the supplied value order. It says nothing about unmatched values, errors on a bad column name, case sensitivity, 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 sentence with no filler, and the core selection rule is front-loaded ahead of the usage hint. The phrasing is slightly convoluted with the embedded 'whose column is any of the given values' clause, but nothing is wasted.

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?

There is no output schema, no annotations, and no parameter documentation, yet the description is only one sentence. An agent cannot tell what a matched row looks like, what happens if a value matches nothing, or what happens if the column does not exist, which are exactly the gaps this description needed to fill.

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 0%, so the description must compensate. It does map both parameters functionally: 'column' is the field being matched and 'values' is the set to match against, with ordering of the output tied to the order of the given values. It still leaves ambiguities such as whether 'column' is a display name versus an id, and it says nothing about the 2-to-10 item constraint.

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 pins down the exact resource (rows of the Payroll Services Quotes dataset), the selection rule (rows whose column matches any given value), and the output ordering (in the order given). That is a specific, testable purpose, though it never explicitly names or contrasts itself with siblings like dataset_search or dataset_row, so an agent must infer the routing.

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 trailing phrase 'for "X vs Y" questions' gives a usable trigger condition, which is more than nothing. However, it offers no when-not guidance and never names an alternative tool, so the agent has to infer how this differs from dataset_search or dataset_row.

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