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poll_rodin_job_status

Poll the status of a Hyper3D Rodin generation task to determine if it has completed, failed, or is still processing.

Instructions

Check if the Hyper3D Rodin generation task is completed.

For Hyper3D Rodin mode MAIN_SITE:
    Parameters:
    - subscription_key: The subscription_key given in the generate model step.

    Returns a list of status. The task is done if all status are "Done".
    If "Failed" showed up, the generating process failed.
    This is a polling API, so only proceed if the status are finally determined ("Done" or "Canceled").

For Hyper3D Rodin mode FAL_AI:
    Parameters:
    - request_id: The request_id given in the generate model step.

    Returns the generation task status. The task is done if status is "COMPLETED".
    The task is in progress if status is "IN_PROGRESS".
    If status other than "COMPLETED", "IN_PROGRESS", "IN_QUEUE" showed up, the generating process might be failed.
    This is a polling API, so only proceed if the status are finally determined ("COMPLETED" or some failed state).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_idNo
subscription_keyNo
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the polling nature and expected final states (Done, Failed, Canceled, COMPLETED, IN_PROGRESS, IN_QUEUE). However, it lacks details on timeouts, retry expectations, or consequences of polling before readiness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is verbose, repeating similar structure for each mode. It could be condensed into a simpler explanation without losing meaning. The front-loading is decent but gets buried in details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (two modes, no output schema, no annotations), the description partially covers return values and termination conditions. However, it omits guidance on parameter combination handling and error recovery, leaving gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description must explain parameters. It maps 'subscription_key' to MAIN_SITE and 'request_id' to FAL_AI, but it doesn't clarify that only one is needed based on mode or how to choose which to provide. Both are optional in schema, leading to ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it checks if Hyper3D Rodin generation is completed and distinguishes between two modes (MAIN_SITE and FAL_AI). However, it's not immediately clear which mode applies or how the tool determines mode, and the purpose is muddled by the lengthy mode-specific details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides guidance on when to proceed based on final status values but fails to differentiate this tool from siblings like 'get_hyper3d_status' or 'poll_hunyuan_job_status'. No explicit when-to-use or when-not-to-use advice is given.

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

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