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

There are no annotations, so the description carries the behavioral disclosure burden. It adds useful behavioral details: case-insensitive matching and a 50-row cap. However, it does not state default limit behavior, output shape, or behavior when no rows match.

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 compact sentence with no filler. The resource and core matching behavior are front-loaded, and every clause contributes meaningful information.

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 the main semantic points: what is searched, how matching works, and the result cap. The absence of an output schema and explicit sibling routing keeps it slightly short of complete, but the core call is well specified.

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 covers query semantics via its description, and the description adds case-insensitivity for query and 'up to 50' context for limit. Because schema description coverage is only 50%, the description partially compensates but does not clarify whether limit is optional or what the default is.

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 resource (rows of the Xlifflane dataset), the matching behavior (cells contain the query), and the case-insensitive rule. It does not explicitly differentiate itself from siblings like dataset_row or dataset_top, but the filtering semantics are specific enough to be understood independently.

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 gives no guidance on when to use dataset_search versus sibling tools such as dataset_row, dataset_top, or dataset_columns. It implies a search use case but offers no contextual conditions, exclusions, or alternatives.

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