superset_dataset_create

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

With no annotations, the description carries the transparency burden. It discloses the physical/virtual dual behavior, explains schema default behavior, and states the return format. It omits permission requirements and error scenarios but provides more than minimal behavioral 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?

The description is well-structured with an intro, parameter list, and return type. Every sentence contributes meaning, with no fluff or repetition. It is appropriately sized for the tool's complexity.

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 physical/virtual distinction and the availability of an output schema, the description covers the tool's purpose, parameter usage, and return format. It is complete enough for an agent to select and invoke the tool correctly in most scenarios.

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 fully compensates by explaining each parameter's meaning and role: table_name, database, schema_name (with default), and sql (for virtual). This adds significant value beyond the raw 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 tool creates a new dataset, distinguishes between physical and virtual datasets with specific definitions, and differentiates it from sibling tools like update/delete/get by focusing on the create action.

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?

It explains when to use physical vs virtual datasets and references superset_database_list for database IDs, providing clear usage context. However, it does not explicitly name alternative tools like superset_dataset_get_or_create or state when not to use this tool.

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