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

With no annotations provided, the description must carry the behavioral disclosure burden. It does disclose case-insensitive matching and that matches can occur in any cell, which is helpful. However, it does not mention default limit behavior, output format, or whether results are sorted or paginated.

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 that gets straight to the point. It front-loads the key behavior and avoids filler.

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 only two parameters, the description covers the core invocation details: what is searched, where it is searched, case sensitivity, and the result cap. It does not specify default limit or output shape, but the low complexity makes this a minor gap.

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 describes the query parameter as 'text to look for in any cell', so the description adds limited new parameter-level meaning. The limit parameter has no description in the schema, and the tool description only restates the maximum of 50 rather than explaining its default or effect.

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 specific verb and resource: it returns dataset rows whose cells contain the query. It clearly differentiates this search tool from sibling tools like dataset_columns, dataset_row, or dataset_top by describing cell-level substring matching.

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 makes the tool's purpose clear enough that an agent can infer when to use it, but it does not explicitly state when to prefer an alternative. No sibling tools or exclusion criteria are mentioned, so guidance is implied rather than explicit.

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