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poll_rodin_job_status

Check completion status of Hyper3D Rodin 3D model generation tasks in Blender, using subscription keys or request IDs to monitor progress until final determination.

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 main handler function for the 'poll_rodin_job_status' MCP tool. It forwards the polling request to the Blender addon via socket command, using either a subscription_key (MAIN_SITE mode) or request_id (FAL_AI mode). Includes input validation via parameters and comprehensive docstring describing usage for both modes. The @mcp.tool() decorator registers it with the FastMCP server.
    @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
        except Exception as e:
            logger.error(f"Error generating Hyper3D task: {str(e)}")
            return f"Error generating Hyper3D task: {str(e)}"
  • Function signature providing type hints and default values for tool parameters: subscription_key (str, optional for MAIN_SITE mode) and request_id (str, optional for FAL_AI mode). The docstring provides detailed input/output schema description.
    def poll_rodin_job_status(
        ctx: Context,
        subscription_key: str=None,
        request_id: str=None,
    ):
  • Usage instruction in the asset_creation_strategy prompt, guiding when and how to use the poll_rodin_job_status tool as part of the Hyper3D generation workflow.
    2. Poll the status
        - Use poll_rodin_job_status() to check if the generation task has completed or failed
    3. Import the asset
        - Use import_generated_asset() to import the generated GLB model the asset
  • The @mcp.tool() decorator registers the poll_rodin_job_status function as an MCP tool with the FastMCP server instance.
    @mcp.tool()
Behavior4/5

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's polling nature, status interpretation rules for both modes, and failure conditions. It explains what the tool returns and how to determine completion/failure, though it doesn't mention rate limits or authentication needs.

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

Conciseness4/5

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

The description is appropriately sized and well-structured with clear sections for different modes. Each sentence adds value by explaining parameters, return values, and usage rules. While comprehensive, it could be slightly more concise by combining some status interpretation rules.

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 the complexity of supporting two operational modes with different parameters and status interpretations, the description provides substantial context. With no annotations and no output schema, it explains return formats, status meanings, and failure conditions. It could benefit from explicitly mentioning that only one parameter should be used at a time.

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

Parameters5/5

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

The input schema has 0% description coverage, so the description fully compensates by explaining both parameters in context. It clearly defines 'subscription_key' for MAIN_SITE mode and 'request_id' for FAL_AI mode, linking them to previous generation steps and specifying which parameter to use based on the operational mode.

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

Purpose5/5

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), distinguishing it from sibling tools like 'get_hyper3d_status' which likely provides different status information.

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

Usage Guidelines5/5

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 distinguishes between two modes (MAIN_SITE and FAL_AI) with different parameters and status interpretations, offering clear alternatives within the tool itself based on context.

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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