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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the action but fails to mention whether the operation is asynchronous, if it returns a job ID, if it can be called repeatedly, or any side effects on existing models.

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 two sentences with no wasted words. It front-loads the action and immediately follows with usage scenarios, making it easy to parse quickly.

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 absence of annotations and output schema, the description should provide more context about the retraining process, such as whether it's asynchronous, typical response format, and implications for dataset queries. It lacks critical operational details.

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 input schema has 0% description coverage for parameters, and the description provides no additional meaning for 'dataset_id' or 'epochs'. It does not explain what 'epochs' controls or how 'dataset_id' is used, leaving the agent to rely on parameter names alone.

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 it re-triggers semantic training for a dataset and gives specific use cases (schema changes, alias updates, fresh build). It distinguishes from siblings like 'onboard_dataset' and 'get_dataset_status' by focusing on retraining, though it doesn't explicitly name alternatives.

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 explicitly tells when to use the tool: after schema changes, alias updates, or to force a fresh model build. This provides clear context for usage, though it does not include when not to use or mention alternative tools.

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