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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the exact-match and case-insensitive behavior, which is useful, but does not mention return format, handling of multiple matches, or error conditions (e.g., missing column). The description is not misleading, but it is minimal and leaves several behavioral aspects unspecified.

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 concise sentence that front-loads the core action and matching rule. It avoids redundancy and contains no filler. Every word contributes to the meaning, making it efficient and easy to parse.

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 tool, the description explains the matching rule but omits essential usage context: it does not indicate that 'column' should be an existing column name (and how to discover that), whether all matching rows are returned, or what the output looks like. With no output schema and no annotations, these gaps leave an agent uncertain about invocation details and result interpretation.

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?

The schema description coverage is 0%, so the description must compensate for parameter semantics. It mentions 'a column equals a value' which maps to the 'column' and 'value' parameters, but does not clarify what constitutes a valid column name, how to obtain it, or the expected format of the value (e.g., string vs numeric). The description adds little beyond the schema's type and minLength constraints, leaving agents to infer parameter meaning.

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 that the tool returns rows where a column equals a value exactly, and specifies case-insensitivity. The title 'Look a row up by an exact key' reinforces the purpose and differentiates it from dataset_search, though it does not explicitly name sibling tools. This is clear enough for an agent to grasp the core function.

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 use for exact-match lookups ('equals a value exactly'), which distinguishes it from a fuzzy search like dataset_search. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites such as knowing valid column names or how to obtain them. Guidance is implied but not actionable beyond the matching condition.

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