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

With no annotations, the description carries the full behavioral disclosure burden. It does disclose meaningful behavior: case-insensitive matching and a 50-row cap. It does not mention return format, ordering, or no-match behavior, but these are relatively minor 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?

The description is a single sentence with no filler. It front-loads the search behavior and includes the key constraints without redundancy.

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 output schema, the description covers the matching behavior, case sensitivity, and result cap. The lack of explicit return-format details is a minor gap given the simple 'rows' framing.

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 only documents the query parameter, while the limit parameter is left with no description. The tool description adds value by clarifying case-insensitive matching for the query and implying that results are capped at 50, which gives the limit parameter meaning.

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 that the tool returns rows whose cells contain the query, with case-insensitive matching and a maximum of 50 results. This is specific enough to distinguish it from sibling tools like dataset_columns or dataset_top, which suggest schema or aggregation operations.

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 usage is inferable: search across all cells for matching text. However, there is no explicit when-to-use guidance, no mention of alternatives, and no exclusions, so the agent must infer the boundary against sibling tools on its own.

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