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

With no annotations provided, the description carries the behavioral burden. It discloses case-insensitive matching, cell-level containment semantics, and the 50-row cap. It does not cover empty-result behavior or output shape, but those are less critical for a simple search tool.

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 sentence with no filler. The key matching semantics are front-loaded, and the row cap is included as essential operational detail.

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 two-parameter search tool with no annotations and no output schema, the description conveys the core behavior adequately. Minor gaps remain around output structure and behavior when no rows match, but the essential calling context is complete.

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%, but the description compensates meaningfully: it clarifies that query matching is case-insensitive and that results are capped at 50, directly illuminating the limit parameter that lacks a schema description.

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 behavior: returning rows whose cells contain the query, case-insensitively, up to 50. This clearly distinguishes the tool from siblings like dataset_stats or dataset_row, which serve different purposes.

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 clear situational context: use this when you need rows matching a text query across any cell. It does not name alternatives or exclusions, but the intent is unambiguous enough to guide selection.

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