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

Beyond the annotations, the description adds that the call is safe and non-destructive ('Costs nothing to run and builds nothing'), that the created object is empty, and that dataset_id is the value consumed by downstream build tools. This adds meaningful behavioral context without contradicting the readOnlyHint, idempotentHint, or destructiveHint values.

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 front-loaded with the core action and return value, then adds domain context, an example, and a clear next step. Some lifecycle prose is slightly discursive, but each sentence contributes to an agent's understanding of when and how to use the tool.

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 low-complexity create tool with two simple parameters and no output schema, the description covers inputs, return shape, identifier roles, side-effect/cost profile, and the next step in the build flow. An agent has enough information to call it successfully and interpret the returned ids.

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 example payload is valuable and shows realistic values for both name and description. It also explains the role of the returned identifiers (dataset_id vs cloud_dataset_id), though it does not explicitly discuss optionality, uniqueness, or formatting rules beyond what the schema already encodes.

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 opening sentence names the exact verb ('Creates'), the object ('an empty dataset'), the scope ('in your workspace'), and the result ('returns its ids'), making the tool's function immediately identifiable. It also clarifies what a dataset is in this domain ('container a recipe binds to'), distinguishing creation from recipe/run tools such as register_recipe or start_run.

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 provides clear workflow context: the dataset is an empty container that exists before a recipe or rows, and it ends with an explicit next step ('write a recipe and call register_recipe with this dataset_id'). It does not, however, name alternatives such as update_dataset or list_my_datasets, nor give conditions for when not to use the tool.

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