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

With no annotations, the description must carry the full behavioral burden. It does disclose one real behavior not in the schema: rows are returned in the order the values were supplied. It omits whether this is a read-only retrieval, how many rows/columns come back, whether matching is case-sensitive, or what happens with unknown column names, leaving meaningful gaps.

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 selection semantics come before the use-case hint. The fragmentary grammar ('The rows ... whose column is ...') costs it a little clarity but nothing is padded.

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 2-parameter, no-annotation, no-output-schema tool the description covers selection and ordering, but leaves the agent guessing about output shape, valid column values, match semantics, and the 10-value cap. It is the minimum viable description rather than a complete one.

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 is the only source of parameter meaning. It does convey that 'column' selects the field to match on and 'values' is a set of values matched with 'any of' semantics, which adds real information. It says nothing about the 2–10 item bounds on 'values' or which column names are valid (presumably discoverable via dataset_columns).

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 specifies the resource (rows of the Laser Materials Compare dataset) and the selection rule (a column matching any of the given values, returned in the given order), but it is written as a noun fragment with no verb and never explains how it differs from siblings like dataset_search, dataset_top, or dataset_row, which plausibly also return filtered rows. The 'compare' framing in the name is only loosely supported by the text, so an agent cannot confidently separate it from its neighbors.

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 one implied use case, which is a genuine contextual cue. However, there is no explicit when-not guidance, no named alternative (e.g. dataset_search for single-value lookup), and no statement of when the ordering or side-by-side comparison matters versus other retrieval tools.

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