dashai_job_status

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds meaningful context by explaining the status values (not_started, started, finished, error) and their practical interpretation, which helps the agent decide whether to keep polling.

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 compact and well-structured: a clear purpose line, a brief but valuable status explanation, then Args and Returns sections. Every sentence adds necessary information with no filler or repetition.

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?

This is a simple polling tool, and the description covers purpose, parameter source, return format, and status interpretation. With annotations providing the safety profile, there is no critical missing context.

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?

Although schema_description_coverage is reported as 0%, the description explicitly documents the 'job_id' parameter and clarifies that it is returned when enqueuing. The schema also includes a similar description, so the parameter's meaning is fully covered and not just left to schema 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 opens with 'Polls the status of an enqueued job', using a specific verb and resource. It also lists examples (training, prediction, explanation), which distinguishes it from sibling tools like dashai_train_model or dashai_predict.

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 context on when polling is useful, explicitly stating that a 'started' status is worth waiting on while 'error' will not improve with further polling. This gives implicit when-to-use and when-not-to-use guidance, though it does not explicitly name alternative tools.

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