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, all-cell coverage, and the 50-row cap, but it does not describe ordering, default limit behavior, or the exact shape of returned rows. It is adequate for a simple search tool but not fully transparent.

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?

One sentence communicates the operation, dataset, matching rule, and result cap with no filler. The key behavioral detail, case-insensitive cell matching, is front-loaded.

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 low-complexity read/search tool with no output schema and no annotations, the description covers the essential invocation knowledge: what is searched, how matching works, and the result limit. Minor gaps like default limit and return-row shape do not prevent correct use.

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%: query has a description while limit does not. The description compensates by clarifying that the query matches case-insensitively in any cell and that results are capped at 50, adding meaning beyond the bare parameter names and 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 names a specific verb/resource ('Rows of the Card Machine Pricing dataset') and states the matching semantics: cells containing the query, case-insensitive. This clearly distinguishes it from sibling tools like dataset_stats, dataset_columns, or dataset_row.

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 clear: use it to find rows by arbitrary text in any cell. However, it does not explicitly name alternatives or exclusion conditions, so the agent must infer when this tool is preferred over sibling tools rather than being told.

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