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 key traits: it's a polling API (implying repeated checks), outlines status outcomes ('Done', 'Failed', 'COMPLETED', etc.), and specifies when to act based on final states. It doesn't cover rate limits or auth needs, but given the context, this is reasonably comprehensive for a polling tool.

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 appropriately sized but could be more front-loaded. The first sentence states the purpose clearly, but the subsequent detailed breakdown into two modes, while informative, makes it slightly verbose. Every sentence adds value, but the structure could be tightened for quicker scanning.

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 (two operational modes with different parameters and statuses), no annotations, and no output schema, the description does a good job of being complete. It explains what the tool does, how to use it, parameter semantics, and expected behaviors. However, it lacks details on error handling or response formats, leaving some gaps for a tool with such varied modes.

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?

Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the schema by explaining that 'subscription_key' is used for MAIN_SITE mode and 'request_id' for FAL_AI mode, linking each parameter to specific contexts and their origins ('given in the generate model step'). This clarifies when and why to use each parameter, effectively documenting both parameters.

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 verb ('check') and resource ('Hyper3D Rodin generation task'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status' or 'poll_hunyuan_job_status', which appear to serve similar polling functions for different services.

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 usage guidance by detailing two distinct modes (MAIN_SITE and FAL_AI) with specific parameter requirements and status interpretations. It instructs when to proceed ('only proceed if the status are finally determined') and includes conditional logic for handling failures, offering clear operational context without alternatives being necessary here.

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