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

No annotations exist, so the description carries the full burden; it does disclose useful traits: case-insensitive matching, the 50-row cap, and the target dataset. It omits return format, no-match behavior, pagination, and any permission requirements, so the behavioral picture is only partial.

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 with no filler; the resource scope and matching rule come before the cap. It is efficient, though its extreme terseness is part of why other gaps go unfilled.

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 two-parameter search with no output schema and no annotations, the description covers the essentials of scope and cap but says nothing about result shape, ordering, or behavior on zero matches, leaving the no-annotation burden only partly met.

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 50%: query is documented as 'text to look for in any cell' but limit is bare. The description compensates for limit's ceiling ('up to 50') and reinforces that the query matches cells rather than a field, so it adds some value but leaves the limit's default and floor unaddressed.

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 verb+resource: it returns rows of the Wen Receipts dataset matching a query. That is clear and concrete, but it never differentiates itself from the many dataset_* siblings (dataset_row, dataset_top, dataset_stats), so an agent must infer the distinction.

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?

There is no explicit when-to-use guidance and no named alternative. The agent gets the mechanics of the search but nothing about when this beats dataset_top, dataset_row, or entities_search.

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