create_dataset

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

With no annotations, the description carries the transparency burden. It discloses that the dataset is created through an execution for provenance, notes that the dataset is empty, and lists the exact return fields (status, rid, description, dataset_types, version, execution_rid). This gives a solid behavioral picture, though it doesn't explore edge cases like failure modes or sync/async behavior.

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: a clear one-sentence summary, followed by context, usage pointer, args, returns, and an example. Every section earns its place, and the overall length is appropriate 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 simple tool (3 optional params) and the presence of an output schema, the description is complete. It covers the execution context, the population step, label vocabulary, argument semantics, return fields, and provides an example. There are no major gaps.

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 fully explain parameters. It does so with an Args section that clarifies description ('Human-readable description of the dataset's purpose'), dataset_types ('Type labels from Dataset_Type vocabulary' with example), and version ('Initial version string, default 0.1.0'), adding contextual meaning 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 opens with a specific verb+resource: 'Create a new empty dataset within an execution context.' This clearly distinguishes it from sibling tools like add_dataset_members (which populates) and delete_dataset (which removes), while also mentioning the execution context for provenance.

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 states when to use the tool (to create an empty dataset in an execution) and explicitly directs users to add_dataset_members() for population, providing a clear workflow. It doesn't explicitly state when not to use it, but the context and sibling differentiation are adequate.

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