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

With no annotations, the description carries the full burden of behavioral disclosure, and it does disclose meaningful matching behavior — exact, case-insensitive comparison. But it leaves a genuine ambiguity: the title says 'a row' (singular) while the description says 'The rows' (plural), so an agent cannot tell whether one or many matching rows are returned, and no-match behavior is unmentioned. That ambiguity keeps this at a 3 rather than higher.

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?

One compact sentence plus a direct title, with the key matching semantics (exact, case-insensitive) front-loaded. Minor awkwardness in the grammar ('The rows of the Capanix dataset where...') and some redundancy between the title's 'exact key' and the description's 'equals a value exactly' prevent a 5.

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 lookup, the description covers the core behavior and identifies the dataset. Yet with no output schema and no annotations, it should also state whether a single row or multiple rows are returned and what happens when no match is found; it does neither. These two missing details make the definition adequate but not complete.

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 coverage is 0% and the parameters have no descriptions, so the description must supply the relationship between the two parameters. It does, by stating that a column equals a value, implying column is the match field and value is the target. It stops short of examples, formats, or clarifying whether value acts as a primary key, so it only partially compensates for the schema gap.

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 title states a specific operation — looking up a row by exact key — and the description narrows the semantics with 'equals a value exactly (case-insensitive).' This differentiates it from search-like siblings such as dataset_search by committing to exact matching. It earns a 4 rather than 5 because the description is slightly tautological ('rows... where a column equals a value') and never names a sibling it is not.

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 'equals a value exactly (case-insensitive)' implies the tool is for exact-match lookups and implicitly contrasts with broader search. However, there is no explicit guidance about when to prefer this tool over sibling tools like dataset_search or dataset_top. Usage is left largely to inference, which is the definition of an implied-but-not-stated guideline.

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