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

With no annotations, the description carries the full behavioral burden, and it only discloses one trait: row order follows the order of the supplied values. It says nothing about what happens when a value matches no row, whether all values must match, rate/permission requirements, or the shape of what comes back.

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 padding, and the dataset plus matching rule come first. The trailing 'for "X vs Y" questions' clause is a slightly awkward bolt-on but it is short and adds routing value.

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 two-parameter read tool with no annotations and no output schema, the description covers the core selection logic but leaves gaps an agent needs: what a returned row contains (all columns or just the matched one), single-versus-multi match behavior, and unmatched-value handling. Not fatal, but not complete.

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, and it partially does: it clarifies that 'column' refers to a dataset column and that 'values' are matched against it with order preserved. It still does not say how to discover valid column names (dataset_columns) or that at most 10 values are accepted, which are left to the schema.

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 names the concrete resource (rows of the Insurance by Profession dataset) and the selection rule (column matching any of the given values), which distinguishes it from a general search or a single-row fetch. It is framed as a noun phrase rather than a verb, so the 'compare' framing from the title is only implied by the trailing 'for "X vs Y" questions' clause, but an agent can still tell what it returns.

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?

'for "X vs Y" questions' is a real usage cue that tells the agent the intended question shape. However, it names no alternatives (e.g., dataset_row or dataset_search) and gives no exclusions or prerequisites, so when-not-to-use is left entirely to inference.

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