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

With no annotations at all, the description carries the full behavioral burden. It does disclose one real trait — rows come back in the order of the supplied values — but says nothing about permissions, result size, truncation, or what happens when a value matches nothing, and the values cap of 10 is never surfaced.

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?

It is a single short sentence with no padding, which is good. But it is a sentence fragment whose key information (what is returned) is buried in a relative clause, and the hardcoded dataset name consumes most of the opening without adding discriminating power.

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 tool with no annotations and no output schema, the definition should state what the response looks like and how it differs from the other dataset_* readers. Neither is present, and the unmentioned 10-value ceiling is a practical constraint an agent could easily violate.

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 matching is an any-of (OR) test across values and that the order of values governs output ordering. It adds nothing about value format, case sensitivity, or the 2–10 item bounds, so the compensation is incomplete.

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 is a noun phrase rather than a verb-led statement: it returns rows of a named dataset filtered by a column matching any of the given values. The filter and ordering semantics come through, but the absence of a verb and of any distinction from siblings like dataset_search or dataset_row leaves the agent to infer that this is a filtered multi-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 a genuine usage cue that implies comparison-style queries. However, it names no alternative tool and states no condition under which dataset_search, dataset_row, or dataset_top would be preferred, so routing is only implied.

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