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

There are no annotations, so the description carries the full behavioral burden. It does disclose useful behavior: matching is case-insensitive, applies to any cell, and returns at most 50 rows. However, it does not describe result ordering, output row shape, pagination, or behavior when no rows match.

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 front-loaded sentence with no filler or redundancy. Every phrase adds meaningful functional detail: scope, matching rule, case behavior, and result cap.

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 search tool with two flat parameters and no output schema or annotations, the description is mostly adequate: it names the dataset, query semantics, and result cap. The main gap is the ambiguity around the limit parameter and the lack of any statement about what information the returned rows contain.

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 documents the query parameter as 'text to look for in any cell', and the description reinforces the cell-containment semantics. However, the limit parameter is not explicitly described in either the schema or the description, so it is unclear whether 50 is a default, a hard maximum, or how requesting fewer rows works.

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 the operation: return rows of the Coshhvo dataset whose cells contain the query, and it adds useful constraints like case-insensitivity and a 50-row cap. This distinguishes it from siblings like dataset_stats or dataset_row, which clearly 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 intended use is clear: choose this tool when you need to find dataset rows by arbitrary cell text. It does not explicitly name alternatives or say when not to use it, so it misses the top score, but the semantic context is strong enough for an agent to route correctly.

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