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

No annotations are provided, so the description carries the full behavioral burden. It conveys that multiple rows are returned in the given value order, but says nothing about result size limits (schema allows up to 10 values), failure behavior for unknown columns, or the shape/size of the returned rows. For a query tool with zero annotation coverage this is thin.

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 with no filler, and the key ordering semantics are surfaced at the end where they matter for a comparison tool. It is slightly front-heavy with dataset qualification rather than the action.

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?

With no output schema and no annotations, the description must stand alone, and it adequately covers the input side (column + values + order) but leaves the return shape and edge cases undocumented. Minimum viable for a two-parameter read tool whose output an agent can inspect directly.

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 add real meaning: it clarifies that 'values' is matched against 'column' and that output order follows input order, which the schema does not state. It still omits details like whether column names must match dataset_columns output exactly and what the maxItems=10 constraint means in practice.

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 resource (rows of the Insurance by Profession dataset) and the filter semantics (rows whose column is any of the given values, in order), which is more specific than the title's vague 'Compare rows side by side'. However, it is a verbless noun phrase with no explicit action verb, and it does not distinguish itself from sibling filters like dataset_search, dataset_row, or dataset_top.

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 clause 'for "X vs Y" questions' implies the intended scenario and ordering behavior, giving some usage signal. It stops short of naming alternatives (dataset_search, dataset_top) or stating when this tool should NOT be used instead of them.

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