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

With no annotations, the description carries the behavioral disclosure burden. It mentions case-insensitive matching and the 50-row cap, which are useful, but it does not disclose ordering, default limit behavior, pagination, or what a non-matching query returns.

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?

A single front-loaded sentence contains the core behavior and constraints without filler. Every word earns its place.

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?

The description is adequate for a basic substring search call with a required query and optional limit. It lacks an explicit statement of default behavior, output shape beyond 'rows,' or guidance on when the 50-row limit applies, but the core invocation is understandable.

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 already describes query as text to look for in any cell; the description adds case-insensitivity and an implicit 50-result cap, which helps clarify the limit parameter's purpose. The meaning of limit is still mostly inferred rather than explicitly stated.

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 specifies that the tool returns rows from the dataset whose cells contain the query, including the case-insensitive behavior. This distinguishes it from sibling tools like dataset_stats or dataset_columns, though it does not explicitly differentiate itself.

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 intended use is implied: when an agent needs rows matching a text query across all cells. However, it does not state when to prefer this over sibling tools such as dataset_row, nor does it mention exclusions or fallback behavior.

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