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

No annotations are supplied, so the description carries the disclosure burden alone. It does add real behavioral context beyond the schema: results preserve the order in which values were given, and the tool is scoped to one specific dataset. It says nothing about return shape, whether the match is exact/case-sensitive, or the 2–10 value limit's consequences.

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 front-loaded sentence with no filler; the ordering guarantee and the usage hint both earn their place. The em-dash construction is slightly dense but not wasteful.

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 definition leaves important gaps: the shape of returned rows, match semantics (exact vs. substring, case), and what happens at the 10-value cap are all unstated. For a read tool with zero structured field coverage, more disclosure was needed.

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 for both parameters. It partially does: it explains that 'column' selects the matching column and 'values' are matched with any-of semantics and their order matters. It does not say what column names are valid (a sibling, dataset_columns, presumably lists them) or explain the min/maxItems boundary beyond what the schema already enforces.

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 Miniature Paints Compare dataset), the retrieval condition (column matches any of the given values), and the ordering guarantee, so an agent can tell what comes back. It does not explicitly differentiate itself from siblings like dataset_search or dataset_top, but the scoping to a named dataset and the ordered multi-value match make the intent clear. Not a tautology of the name – it clarifies that 'compare' is implemented as an ordered row fetch.

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' gives implied usage context, which is genuinely helpful framing. However, no alternatives are named: an agent is not told when to prefer dataset_search, dataset_row, or dataset_top over this tool, nor any preconditions (e.g. valid column names). Guidance is inferred rather than stated.

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