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

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals case-insensitive matching and a hard cap of 50 results, but it does not mention ordering, empty-result behavior, or whether matching applies to all columns. This is adequate but not rich.

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 efficient sentence with no filler. It front-loads the core behavior, matching rule, and limit in a compact form that is easy to scan.

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 essential selection behavior and limit. It lacks explicit return-format details and no-match behavior, but the absence of an output schema makes some ambiguity acceptable for this level of complexity.

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 coverage is only 50%, with query documented but limit left to schema type and bounds. The description adds 'up to 50' and 'cells contain the query', which helps, but it does not compensate fully for the undocumented limit semantics or specify format constraints.

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 what the tool does: it returns dataset rows whose cells contain the query, with case-insensitive matching and a limit of 50. It is specific about the resource and behavior, though it does not explicitly differentiate itself from siblings like dataset_row or dataset_top.

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 implies the tool is for finding rows by arbitrary cell content, but it gives no explicit guidance on when to prefer it over alternatives or when not to use it. Sibling tools like dataset_row, dataset_top, and dataset_columns exist, yet no routing information is provided.

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