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

No annotations exist, so the description carries full behavioral burden. It usefully discloses case-insensitive exact matching, but it does not clarify whether one row or multiple rows are returned, what happens on no match, or what the response looks like. The title says 'a row' while the description says 'rows', creating ambiguity.

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 focused sentence that front-loads the core matching behavior without wasted words. It is concise and readable, though it could have included sibling or return-info details without becoming bloated.

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 tool with no annotations, no output schema, and a 0% schema description coverage, the description is too thin. It omits the return shape, multiple-match behavior, error/empty-result handling, and how to discover valid column names, leaving an agent to guess important invocation details.

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 input schema has 0% description coverage, but the description explains the relationship between column and value: the column's value must equal the provided value exactly, case-insensitively. This adds meaning beyond the bare parameter names, though it does not enumerate valid columns or value formatting rules.

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 from a specific dataset where a column equals a supplied value exactly, with case-insensitive matching. The phrase 'exactly' differentiates it from the likely fuzzy dataset_search sibling, though no sibling is named explicitly.

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?

Usage is implied: use this when an exact, case-insensitive column match is needed. However, the description gives no explicit guidance about when to prefer this over dataset_search or dataset_columns, nor does it mention any alternatives or exclusions.

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