train_dataset_delete

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

With no annotations, the description fully handles behavioral transparency. It discloses the irreversible nature and the condition under which it refuses (ongoing training job). This is good but could additionally mention any required permissions or side effects.

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?

Three concise sentences, each adding essential information: what is deleted, when it refuses, and the irreversible nature. No wasted words.

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 delete tool with one parameter, the description covers the essential aspects: action, constraint, and caution. It is complete enough for safe use, though it could mention the return value or outcome upon success.

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?

The parameter 'name' is described in the schema as 'Dataset name (from train_list_datasets).' The description does not add extra meaning beyond the schema. With 100% schema coverage, baseline 3 is appropriate.

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 deletes a whole staged dataset (images + captions), specifying the exact resource and action. It distinguishes itself from related tools like train_dataset_update or train_list_datasets by focusing on deletion of the entire staged dataset.

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 strong usage guidance by stating that the tool refuses if a running/queued job trains from the dataset, and warns of irreversibility with a recommendation to confirm with the user first. This helps the agent avoid misuse, though it does not explicitly compare to alternatives.

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