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

With no annotations, the description carries the behavioral disclosure burden, and it does disclose important traits: case-insensitive matching, matching against any cell, and a hard cap of 50 returned rows. It does not state default limit behavior or the return shape, but the core matching semantics are clearly described.

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, information-dense sentence with no filler. The subject, matching rule, case-sensitivity, and row cap are all front-loaded and every phrase contributes meaning.

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 with one required parameter and no output schema, the description gives enough to invoke it correctly: what to query, how matching works, and the result cap. It lacks an explicit statement of returned row fields and sibling-tool selection guidance, but the operation is simple and the description covers the essential 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 description adds meaning beyond the schema by clarifying that query matching is case-insensitive and cell-based, and by framing the limit as a 50-row cap. The schema's limit field only has min/max constraints, so the natural-language cap meaning is useful. It stops short of explaining default behavior if limit is omitted, but for two parameters this is solid coverage.

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 identifies a specific behavior: return dataset rows whose cells contain the query text, with case-insensitive matching and a 50-row cap. It is more specific than the generic title and clearly positions the tool as a cell-content search rather than a stats or row-lookup tool. It does not explicitly name sibling tools, but the function itself is unambiguous.

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?

There is no explicit guidance on when dataset_search should be used instead of dataset_row, dataset_stats, dataset_top, or related siblings. The intended use is only implied by the search behavior. No alternatives, exclusions, or preconditions are mentioned.

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