Derive Tabular Dataset

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

The description adds useful behavioral context beyond the annotations: it states the output is immutable and that identical inputs yield the same derived dataset ID, which concretizes the idempotentHint. It does not contradict the annotations.

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?

The description is short, front-loaded, and every sentence contributes meaningful information. There is no filler or repetition of the tool name.

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?

Given the required nested typed_params object and the lack of any schema commentary, the description is not complete enough for an agent to know what parameters to pass for each supported profile. The output schema may cover return values, but the input side still has a critical documentation 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?

With 0% schema description coverage, the description must compensate. It does explain transform_profile_id values and expected_manifest_hash requirements, but it leaves typed_params completely unspecified even though it is a required free-form object. source_dataset_id is also only implied by its name, so the most ambiguous parameter has no semantic guidance.

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 uses a specific verb and resource ('Run one product-owned tabular transform') and makes the output clear ('new immutable dataset'). This clearly separates it from sibling tools like register_dataset or preview_dataset, and the supported profiles add further specificity.

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 when to use the tool by listing supported profiles and requiring an exact manifest hash, but it does not explicitly state when not to use it or point to alternatives. Usage context is inferable rather than directly stated.

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