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

With no annotations, the description carries the full behavioral burden, and it does disclose two important behaviors: filtering by any of the given values and preserving the order given. It does not state read-only nature, permission needs, result limits, or what happens when no rows match.

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?

The description is a single compact sentence with no wasted words, and it contains only one idea. Its structure is slightly weak because it opens with a noun phrase rather than the action, but it is appropriately sized for the tool.

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 simple two-parameter read tool, the description conveys the core selection and ordering semantics. It does not explain parameter constraints or error behavior, though the input schema at least carries the structural validation rules.

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 conceptually explains both parameters by describing the column filter and the ordered list of values, but it omits constraints such as the minimum of 2 and maximum of 10 items and the string-only type.

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 indicates that the tool returns rows from the Rechner HQ dataset filtered by a column's values, and it hints at comparison use with "X vs Y" questions. However, it is phrased as a noun fragment rather than a clear verb+resource statement, and it does not explicitly distinguish itself from siblings like dataset_search or dataset_row.

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 phrase "for 'X vs Y' questions" gives an implied usage scenario, which is better than no guidance. But it does not name alternatives or state when-not to use this tool, so the agent must infer how it differs from dataset_search or dataset_top.

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