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

With no annotations present, the description must carry the behavioral burden. It clearly indicates a read-only operation (selecting rows), which is a positive. However, it does not describe edge cases such as empty results, case-insensitivity limits, or any error behavior, and it does not explicitly state that it does not modify data, though that is strongly implied.

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 extremely concise—a single sentence with no unnecessary words. It delivers the essential information without fluff, making it easy for an agent to parse quickly.

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 search tool, the description covers the main context: what dataset is searched, what counts as a match, and the result size cap. The absence of an output schema means the agent does not know the exact result format, but the description's 'rows' phrasing gives a reasonable hint. Overall, it is sufficient for the task.

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 already describes both parameters, and the description adds useful semantic detail by specifying that the query is matched against cells and that matching is case-insensitive. It does not elaborate on the 'limit' parameter beyond the schema's min/max, but the core semantics are well covered.

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 tool's purpose: it searches the Lanyardo dataset for rows whose cells contain the given query. The behavior is unambiguous and easily distinguishable from the sibling tools by its search-and-filter nature.

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 does not provide explicit guidance on when to use this tool versus alternatives such as dataset_row or dataset_stats. It does not mention any prerequisites or conditions that would make this tool the preferred choice, leaving the agent to infer applicability from the name and behavior.

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