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

With no annotations, the description carries the behavioral burden and does disclose useful traits: case-insensitive matching, substring/cell containment, and a maximum of 50 rows. It does not mention ordering, empty-result behavior, or a default limit, which are material gaps 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?

A single front-loaded sentence states the target dataset, the matching rule, case-sensitivity behavior, and the row cap with no filler. Every element earns its place.

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 low-complexity search tool with no output schema, the description provides the essential invocation contract: what is searched, how matching works, and the cap. It is slightly incomplete regarding result ordering and defaults, but those are minor for selecting and calling the tool.

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 covers only the query parameter; the description compensates by adding case-insensitivity to the query and clarifying the limit as 'up to 50'. This adds real meaning beyond the bare integer-field schema.

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 uses a specific verb and resource: returning 'Rows of the Medcontra dataset' that contain the query, including case-insensitivity and a 50-row cap. This is clear, but it doesn't explicitly differentiate from siblings like dataset_row or dataset_top, so it falls short of a 5.

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 description implies the tool is for finding rows by substring match across cells, which is a clear use context. However, it never states when not to use it or names alternatives, leaving the agent to infer the boundary against the sibling tools.

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