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

With no annotations, the description carries the full burden. It usefully discloses case-insensitive cell containment and a 50-row cap, but it does not clarify the return shape, default limit behavior, ordering, or what happens when there are no matches. This is adequate but incomplete.

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 sentence that front-loads the dataset and matching condition, with no filler or redundancy. Every word contributes meaning, and the case-insensitivity plus cap are efficiently included.

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, the basics are covered, but the lack of an output schema and annotations means the description should say more about return format, default limit, and empty-result behavior. It is functional but leaves important edge cases to be discovered.

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 and gives a range for limit. The description adds case-insensitive behavior and an upper bound of 50, but it does not explain the default for the optional limit or how the limit parameter maps to the returned row count.

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 indicates that the tool returns rows of the Tieoutly dataset that match a query in any cell, with case-insensitive matching and a 50-row cap. It does not explicitly name sibling tools, but the search-oriented behavior is distinct and easy to recognize.

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: use this tool when you need rows whose cells contain a given search string. However, there is no explicit guidance about when to prefer this over siblings like dataset_row or dataset_top, nor any when-not-to-use instructions.

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