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

With no annotations, the description carries the burden of disclosure. It does reveal useful behavior: case-insensitive, full-cell matching, and a result cap of 50. However, it does not mention output format, ordering, empty-result behavior, or error handling.

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 dense sentence with no filler. It front-loads the primary behavior and packs the important matching details and limit into minimal words.

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 search tool, the description covers core semantics and the result cap. But with no output schema and no annotations, it omits return-value details and sibling guidance, leaving some ambiguity for an agent deciding how to interpret or compare results.

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 only 50%: query has a schema description, but limit does not. The tool description compensates by explaining the query matches any cell case-insensitively and implying the 50-row cap via limit. This adds meaningful semantic value beyond the raw schema.

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 states a precise behavior: return rows whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes dataset_search from siblings like dataset_row, dataset_top, and dataset_columns.

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 given about when to use this tool versus alternatives like dataset_row or dataset_top. The intended use case is only implied by the description, with no exclusions or comparison to siblings.

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