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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses case-insensitive substring matching and a 50-row limit, which are important behavioral traits. It does not mention ordering or pagination, but for a simple read-only search tool these are minor gaps.

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 one tight, front-loaded sentence. Every word adds value, and there is no filler or repetition.

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?

The tool is simple, has only two parameters, and the description covers the core behavior and the main constraint (up to 50 rows). It does not describe ordering or default limit, but these are not critical for an agent to invoke the tool correctly.

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%: the query parameter already has a description, and the description reinforces it with 'cells contain the query' and adds case-insensitivity. The limit parameter is not described in the schema, and the description only repeats the 50 max already present in the schema, adding no meaningful new semantics.

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 states exactly what the tool does: returns rows of the Curtilo dataset whose cells contain the query, and it adds specific behavior (case-insensitive matching, 50-row cap). This clearly distinguishes it from sibling tools like dataset_columns or dataset_stats, which 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?

Usage context is implied: use this when you need dataset rows matching a text query. However, there is no explicit guidance about when not to use it or how it compares to siblings such as dataset_row or dataset_top.

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