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

Annotations cover the safety profile (readOnlyHint false, destructiveHint false, idempotentHint true), and the description adds substantial context beyond them: 'Costs nothing to run and builds nothing,' the reuse behavior returning an existing empty dataset with reused_open_request: true, and the fact that the given name is a working title replaced by the first update_dataset with a changed description. This is rich behavioral disclosure, and it is consistent with the idempotentHint.

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?

Purpose and scope are front-loaded, and the dense detail (naming convention, return shape, reuse semantics, next step) is justified for a 0%-coverage, no-output-schema tool. The 'which is the point — the page opens on it and fills in' clause is a touch of narrative padding that could be trimmed.

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?

With no output schema, the description supplies the return shape ({dataset_id, cloud_dataset_id, name, description, dashboard_url}) and clarifies which id downstream tools consume. Combined with the workflow guidance and reuse rule, an agent has everything needed to call this correctly.

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, and it largely does: it gives detailed naming rules for 'name' (subject then place, never a grain word, mechanism, publisher or station code), explains that 'description' acts as a working title that update_dataset later replaces, and pins down 'separate'. It stops short of full syntax/limit detail, so it is strong but not exhaustive.

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?

Opens with a specific verb+resource+scope ('Creates an empty dataset in your workspace and returns its ids') and goes further to define what a dataset is conceptually ('the container a recipe binds to'). An agent can separate it from get_dataset, update_dataset, connect_dataset and search_datasets purely from this text.

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?

Gives explicit when-not guidance ('If the user handed you a dataset id from its page, build into that id and do not call this'), a next-step workflow (write a recipe, call register_recipe with this dataset_id), and the precise condition for the separate flag ('only when the user has asked for another, separate dataset'). Nothing is left to inference.

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