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

With no annotations, the description carries the full behavioral burden. It does disclose result ordering ("in the order given"), which is a real trait, but says nothing about read-only scope, what happens when a value is absent from the column, or how the implicit Stocktaka dataset is selected.

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?

Single short fragment with no filler, so it is tight. But it is grammatically incomplete and front-loads the return shape rather than the action, making it slower to parse than a verb-first sentence would be.

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-required-parameter tool with zero schema coverage, no annotations, and no output schema, the description is too thin: it never states the dataset source assumptions, the 10-value cap, or error behavior on misses. An agent can guess but cannot be certain how to invoke it correctly.

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 usefully clarifies that matching is any-of (IN semantics) on one column and that output follows input order — semantics the schema does not express. It still omits the value-count constraints (2-10), duplicates/invalid values, and whether 'column' is a header name or index.

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?

It names a concrete resource (rows of the Stocktaka dataset) and a concrete filter (column matches any of the given values), which tells an agent what it will get back. It does not explicitly contrast with the closest sibling (dataset_row, dataset_search), but the 'X vs Y' framing hints at its niche. The phrasing is a noun phrase rather than a verb-led statement, which weakens it slightly.

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" gives a usable when-to-use cue, which is the main thing separating this from the other dataset_* lookups. However, no alternative is named and no exclusion is given (e.g., when to prefer dataset_row for a single entity or dataset_search for substring matching).

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