superset_dataset_duplicate

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

No annotations are provided, so the description carries the burden. It discloses that the copy includes columns and metrics, enforces a unique table_name, and returns a JSON string. However, it does not mention side effects, permissions, or failure behavior for a mutation tool. It is adequate but not rich.

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 concise and well-structured with Args and Returns sections. Every sentence provides useful information, including the important base_model_id naming warning. No filler.

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 tool with an output schema, the description is complete: it states the operation, parameter meanings, constraints, and return format. It could mention prerequisites like the source dataset existing, but that is implied.

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 fully explains both parameters: base_model_id (source dataset ID with a field-name warning) and table_name (unique name for the new dataset). This adds significant meaning beyond the bare schema.

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 action ('Create a copy'), the resource ('existing dataset'), and the scope ('with columns and metrics'). This distinguishes it from sibling tools like superset_dataset_create (which creates from scratch) and superset_dataset_delete.

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 the tool should be used when duplicating an existing dataset, but it does not explicitly contrast it with alternatives or state when not to use it. Sibling tools like superset_dataset_create or superset_dataset_import exist, but no exclusions are provided.

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