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

With no annotations, the description must carry the full burden of behavioral disclosure. It does add one meaningful detail—case-insensitive matching—but it omits whether the tool returns a single row or all matching rows, what happens on no match, and what the response structure looks like. This leaves important behavioral ambiguity for an agent.

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 sentence with no filler, front-loading the core lookup action and the case-insensitive matching rule. It is appropriately brief, though the noun-phrase construction ('The rows...') is slightly awkward and could be more direct as an imperative or verb-first statement.

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 exact lookup, the description covers the basic invocation and matching semantics. But with no output schema and no annotations, it should clarify whether the result is a row or a list of rows and how zero or multiple matches are handled. The case-insensitivity note helps, but the missing return-value behavior is a noticeable gap.

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 does link the two parameters to their roles ('where a column equals a value exactly'), giving basic semantic clarity. However, it does not explain allowed column names, value formatting constraints, or error behavior, so it only partially fills the gap left by the absent schema descriptions.

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 states that the tool returns rows where a column equals a value exactly, and the title 'Look a row up by an exact key' reinforces the intent. This goes beyond a tautology and the 'exactly (case-insensitive)' phrasing helps distinguish it from sibling tools like dataset_search. However, it is phrased as a noun clause rather than a clear verb+resource statement, so it is not maximally explicit.

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 description implies the tool is meant for exact, case-insensitive row lookup, so an agent can infer a basic use case. But it never explicitly states when to prefer this tool over siblings like dataset_search or dataset_top, nor does it mention any exclusions or alternatives. The guidance is implicit rather than direct.

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