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

With no annotations, the description must carry behavioral disclosure. It does state two important traits: the match is case-insensitive and the result is capped at 50 rows. It does not mention ordering, default limit, or behavior when more than 50 rows match, but the core semantics are disclosed.

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 containing only relevant information, with the core behavior front-loaded. No filler or redundancy.

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 description explains what is returned, the case-insensitive nature, and the row cap, which covers the essential behavior for a simple two-parameter search. It lacks details on result ordering, default limit, and output format, and there is no annotation to fill that gap, but the tool is simple enough that this is adequate.

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 only 50% (query documented, limit not). The description adds value by explaining that results are capped at 50 and that the query is matched case-insensitively against any cell, which clarifies both parameters. It stops short of documenting default values or exact matching 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 clearly states the tool returns rows of the EOR Compass dataset matching a case-insensitive query with a 50-row cap. It names the resource and behavior, but does not explicitly distinguish itself from sibling tools like dataset_row or dataset_top, so it misses the top score.

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?

No explicit guidance is given on when to choose dataset_search over siblings such as dataset_row or dataset_top, and no exclusions are stated. The usage is only implied: an agent can infer it should be used to find rows containing arbitrary text, but there is no direct routing information.

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