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

With no annotations provided, the description carries the burden of behavioral disclosure. It does add a useful behavioral trait: case-insensitive exact matching. However, it does not clarify whether one or multiple rows are returned (title says 'a row', description says 'rows'), nor does it describe limits, error behavior, or read-only guarantees. This is a partial disclosure for a simple lookup tool.

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 that quickly conveys the core behavior. It is front-loaded with the dataset name and matching logic, with no filler or redundant phrasing. The grammatical incompleteness is minor and does not hinder comprehension.

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?

Given the tool's simplicity, the description covers the essential invocation details: which parameters are used and the matching semantics. However, the absence of an output schema, combined with the singular/plural ambiguity, leaves the agent uncertain about the return shape. The description also does not mention prerequisites like knowing valid column names, which a sibling tool (dataset_columns) might provide.

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 explain the relationship between the two parameters ('column equals a value') and adds the case-insensitive detail, which is beyond the schema's bare string type and minLength constraints. However, it does not enumerate valid column names, value formatting, or edge cases, leaving some semantic gaps for the agent.

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 clearly states the operation: returning rows from the Lobbyvo dataset where a column exactly matches a value, with a specific case-insensitive behavior. It distinguishes itself from a general search by emphasizing exact match, though it does not name a sibling alternative. The title's singular 'a row' slightly conflicts with the plural 'rows' in the description, creating minor ambiguity.

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?

No explicit guidance is provided about when to use this tool versus siblings like dataset_search or dataset_compare. The exact-match phrasing implies a use case, but the description does not state exclusions or conditions that would route an agent to an alternative. The agent must infer the appropriate context from the tool name and title alone.

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