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poll_rodin_job_status

Check completion status of Hyper3D Rodin generation tasks in Blender, returning results when jobs are done or failed for both MAIN_SITE and FAL_AI modes.

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
subscription_keyNo
request_idNo

Implementation Reference

  • The poll_rodin_job_status function serves as the tool handler, which is registered via @mcp.tool() and @telemetry_tool() decorators. It parses input parameters (subscription_key or request_id) and forwards the command to the blender connection.
    @telemetry_tool("poll_rodin_job_status")
    @mcp.tool()
    def poll_rodin_job_status(
        ctx: Context,
        subscription_key: str=None,
        request_id: str=None,
    ):
        """
        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).
        """
        try:
            blender = get_blender_connection()
            kwargs = {}
            if subscription_key:
                kwargs = {
                    "subscription_key": subscription_key,
                }
            elif request_id:
                kwargs = {
                    "request_id": request_id,
                }
            result = blender.send_command("poll_rodin_job_status", kwargs)
            return result
Behavior4/5

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

With no annotations provided, the description carries full behavioral burden. It discloses return value structures (list of status vs single status), enumerates specific status strings ('Done', 'Failed', 'COMPLETED', 'IN_PROGRESS', 'IN_QUEUE'), explains success/failure conditions, and explicitly identifies this as a polling API. No contradictions with annotations (none exist).

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

Conciseness3/5

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

The description is logically structured by mode (MAIN_SITE vs FAL_AI) but suffers from repetition—the 'This is a polling API...' warning appears twice with nearly identical wording. The content earns its place but could be consolidated into a single unified description without loss of clarity, reducing length by ~30%.

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

Completeness4/5

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

Given no output schema exists and parameters lack schema descriptions, the description adequately covers return value semantics for both API modes, status code interpretations, and polling termination conditions. It provides sufficient context for an agent to understand the complete lifecycle of the polling operation.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates by explaining both subscription_key and request_id, including their provenance ('given in the generate model step') and conditional usage (which parameter for which mode). This provides essential semantic mapping missing from the JSON schema.

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

Purpose4/5

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

The description clearly states the tool 'Check[s] if the Hyper3D Rodin generation task is completed' with specific verb and resource. It distinguishes from generic status tools by specifying 'Rodin' and differentiating between MAIN_SITE and FAL_AI modes. However, it does not explicitly contrast with sibling tool get_hyper3d_status to clarify when to use which Hyper3D status endpoint.

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

Usage Guidelines4/5

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

Provides explicit preconditions: parameters come from 'the generate model step' and mandates 'only proceed if the status are finally determined.' It clearly documents the polling pattern and terminal states ('Done'/'Canceled' or 'COMPLETED'). Lacks explicit 'when not to use' alternatives (e.g., vs initial generation), but covers the critical polling workflow thoroughly.

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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