delete_dataset

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

No annotations are provided, so the description bears the full burden. It transparently explains that the delete is a soft mark that hides from queries while preserving data, and that recurse affects child datasets. It also covers the return structure, which is useful; however, it omits permissions or error conditions.

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 efficiently structured, with a one-line summary followed by essential behavioral details and an Args/Returns section. No redundant sentences are present, and key information is front-loaded.

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 two-parameter tool with a described output, the description covers purpose, behavior, parameters, and return value. It is complete even without annotations, and the recurse behavior is clearly explained.

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 description fully compensates by explaining both dataset_rid and recurse in plain language, including the effect of recurse on nested datasets. The schema only lists types, so this added meaning is essential.

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 phrase 'Soft-delete a dataset (marks deleted but preserves data)' clearly identifies the action and resource. It distinguishes this from other delete operations like delete_catalog, and the 'soft' qualifier differentiates it from potential hard-delete siblings.

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 context on when to use the tool: when you want to hide data without destroying it, and it mentions the recurse option for nested datasets. It does not explicitly list alternatives or exclusions, but the behavioral context is sufficient for an agent to select it appropriately.

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