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

No annotations are supplied, so the description carries the full burden. It does disclose two genuinely behavioral facts: matching is case-insensitive and results are capped at 50 rows. It says nothing about read-only safety, ordering of results, or what happens on zero matches, so the disclosure is partial.

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 result cap are both stated in the minimum words needed.

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?

With no output schema and no annotations, the description should do more. It covers match semantics and result volume but not the shape of returned rows, the default for limit, or result ordering, which an agent needs to interpret the response.

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 ('text to look for in any cell') but limit is not. The description compensates partly by explaining case-insensitive containment matching and the 50-row ceiling, but it does not explain the default limit or the ordering that limit truncates.

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 the resource (rows of the Buffrota dataset), the operation (search), the matching rule (cells contain the query, case-insensitive), and the cap (up to 50). It is clearly distinguishable from dataset_row/stats/top by being a content match rather than a positional or aggregate lookup, though it never names a sibling explicitly.

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?

There is no when-to-use guidance and no mention of alternatives. An agent cannot tell from the text whether to prefer this over dataset_row when it already knows a row id, or over dataset_top when it wants a ranked slice.

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