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

With no annotations, the description carries the burden of disclosure. It covers important behavioral details: search is case-insensitive, matches are across cells, and results are limited to 50. It does not describe every edge case, but the core behavior is transparent enough for a search tool.

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, focused sentence with no filler. The key behavior, matching target, case sensitivity, and result cap are all front-loaded and easy to scan.

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 only two parameters and no output schema, the description covers the essential semantics: what is searched, how matching works, and the maximum result size. It could mention default limit behavior or return format, but these are not critical for correct selection and invocation.

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 but not the limit parameter, so schema coverage is only 50%. The description adds useful context by noting case-insensitivity and a 50-row cap, which partially compensates for the missing limit description, but it does not explicitly tie the limit parameter to 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?

The description clearly states a specific behavior: returning rows of the Enrolvo dataset where any cell contains the query, with case-insensitive matching and a cap of 50. This distinguishes it from sibling tools like dataset_stats or dataset_columns by focusing on content-based row retrieval.

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 implied: call this tool when you need rows matching text in any cell. However, it does not explicitly state when not to use it or mention alternative sibling tools for different scenarios, so an agent must infer usage from the description alone.

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