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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds that the tool returns both columns and filters, which is useful but not a significant behavioral disclosure beyond what the schema hints. No contradictions.

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?

Two sentences, no fluff. The key information (what it does, and the critical usage note about filter keys) is front-loaded and every word 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 single-parameter read-only tool with full schema coverage and no output schema, the description is nearly complete. It lacks an explicit mention of the output format, but the description of 'columns' and 'filters' suffices. Could be a 5, but the lack of output details is a minor gap.

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 only parameter 'dataset' is fully described in the schema with examples, so schema coverage is 100%. The description does not add any extra parameter-specific information beyond what is in the schema, but given full coverage this is adequate.

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 clearly states the tool retrieves a dataset's columns and accepted filters, which is specific and distinct from siblings like query_dataset and list_datasets. It names the resource type (dataset) and the exact information returned.

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 explicitly instructs to use filter keys rather than column names when calling query_dataset, providing a clear when-to-use and a corrective note for a common mistake. It also implies query_dataset is the next step after getting the schema.

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