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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal two meaningful traits: case-insensitive matching and a 50-row cap. However, it does not disclose result ordering, pagination behavior, what happens when more than 50 rows match, or whether the full row or only matching cells are returned.

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 conveys the result type, matching behavior, and limit with zero filler. Every clause adds useful information, and the description is an appropriate size for the tool's simplicity.

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 two-parameter search tool with no output schema, the description covers the essential elements: what is returned (rows), how matching works (cells contain query, case-insensitive), and the hard cap (50). Minor omissions like result ordering and exact return format do not severely impair an agent's ability to invoke it, though more detail would help.

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 has a description, but limit does not. The description adds the case-insensitive detail and clarifies that the output is capped at 50, which partially compensates for the low coverage. It does not explain the default value or behavior of limit, or whether the 50-row cap is applied arbitrarily.

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 a specific action (return rows) and resource (Sowbird dataset), and specifies matching semantics (cells containing the query, case-insensitive) and a result cap (50). This clearly distinguishes it from sibling tools like dataset_columns, dataset_row, and dataset_stats, which address different data views.

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 a full-text search use case but gives no explicit guidance about when to choose this tool over alternatives. It does not mention exclusions, such as 'use dataset_row for a single known row' or 'use dataset_stats for aggregates,' so an agent must infer appropriateness from the name and description.

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