poll_rodin_job_status

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool as a polling API, explains what constitutes completion ('Done' or 'COMPLETED'), failure conditions ('Failed' or other statuses), and provides mode-specific behavioral details. It doesn't mention rate limits or authentication needs, but covers core behavior well.

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 well-structured with clear sections for different modes, but could be more concise. Each sentence earns its place by providing essential information about parameters, returns, and usage guidance. The front-loaded purpose statement is clear, though the detailed mode explanations add length.

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 complexity of supporting two different modes with different parameters and status interpretations, and with no output schema, the description provides substantial context about return values and status interpretation. It explains what constitutes completion, failure, and intermediate states for both modes. Some details like exact response format or error handling could be more explicit.

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 for 2 parameters, the description fully compensates by explaining both parameters in detail: 'subscription_key: The subscription_key given in the generate model step' and 'request_id: The request_id given in the generate model step.' It clearly maps each parameter to specific modes and provides essential context about their origin.

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's purpose: 'Check if the Hyper3D Rodin generation task is completed.' It specifies the exact action (checking completion status) and resource (Hyper3D Rodin generation task), and distinguishes it from sibling tools like 'get_hyper3d_status' by focusing specifically on Rodin jobs.

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 explicit guidance on when to use this tool: 'This is a polling API, so only proceed if the status are finally determined.' It also distinguishes between two modes (MAIN_SITE and FAL_AI) with different parameters and status interpretations, offering clear alternatives within the tool itself.

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