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

No annotations are provided, so the description carries the full disclosure burden. It does add real behavioral facts — matching is case-insensitive and results are capped at 50 — but it says nothing about what happens when more than 50 rows match (truncation vs error), ordering of results, or permissions.

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 with no filler; the matching rule and the cap are delivered together 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 simple two-parameter read tool with no output schema and no annotations, the description covers what is returned (dataset rows), how matching works, and the result ceiling. Minor omissions remain around ordering and behavior beyond 50 matches.

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 50%: 'query' is documented in the schema, 'limit' is not. The description partially compensates by noting the 50-row cap and the case-insensitive cell matching, which clarifies both parameters, but it doesn't state whether limit defaults or how it interacts with the cap.

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?

States a specific verb and resource ('Rows of the Yacht Charter Quotes dataset whose cells contain the query'), plus matching semantics (case-insensitive) and a result cap. It is clear what the tool does, but it never contrasts itself with lookalike siblings such as dataset_row or dataset_top.

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?

No explicit when-to-use statement, no prerequisites, and no named alternative among the many sibling tools. The agent must infer that this is the free-text lookup tool from the phrase 'cells contain the query' alone.

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