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

With no annotations, the description carries the full burden, and it does disclose two real behavioral traits: matching is case-insensitive and results are capped at 50 rows. It omits result ordering, the default when limit is absent, whether matching spans multiple columns or requires all terms, and any performance/rate-limit context.

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 that front-loads the resource and match rule and ends with the result bound. No filler, nothing redundant or buried.

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 read tool with no output schema and no annotations, the description gives the essential result shape and size, but leaves the default limit and result ordering unspecified. An agent can call it, but cannot predict the response ordering or what happens without limit.

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 described in the schema and restated in prose ('cells contain the query'), while 'limit' is only documented via its maximum. 'Up to 50' in the description mirrors the schema's maximum:50 rather than adding new meaning about defaults or ordering.

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 verb+resource: rows of the EntitySearch HQ dataset matching a query, with the case-insensitive any-cell match called out. It is distinguishable from dataset_columns/dataset_stats by the 'rows ... contain the query' phrasing, though it never explicitly contrasts with dataset_row or dataset_top.

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 statement of when to use this tool rather than its many siblings (dataset_top, dataset_row, dataset_compare) and no exclusions or prerequisites. The agent must infer usage from the name alone.

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