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

With no annotations, the description carries the full burden. It describes a read-style retrieval and preserves the order of supplied values, but omits side-effect/read-only disclosure, permissions, return shape, and pagination—notable gaps for a tool with no annotations or output schema.

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 with no filler, and the core retrieval rule is front-loaded. Its structure is a noun phrase rather than a direct action statement, which slightly weakens immediate scanability.

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 retrieval tool, the description covers the basic return concept (rows from the dataset) and use case. But with no output schema and 0% schema description coverage, it should do more to describe the response shape or invocation constraints.

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 description coverage is 0%, so the description must compensate. It names both parameters and adds the key semantic that values are matched against a column and that the order of values is preserved, but it does not clarify equality syntax, column format, or the array min/max constraints.

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?

States a specific resource (rows of the EntitySearch HQ dataset) and the filtering rule (column values), and gives a use-case cue ("X vs Y" questions). It distinguishes the tool from generic search or row retrieval only implicitly, without naming alternatives.

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?

Provides an implied usage context ("for 'X vs Y' questions") but does not state when to prefer this over siblings like dataset_search, dataset_row, or dataset_top. No exclusions or prerequisites are given.

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