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poll_rodin_job_status

Monitor Hyper3D Rodin generation task completion by checking status updates. Determine when 3D models are ready for use in Blender or if generation has failed.

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 handler function for the 'poll_rodin_job_status' MCP tool. It is decorated with @mcp.tool(), registering it with the FastMCP server. The function connects to Blender via a persistent socket connection, constructs parameters based on either subscription_key or request_id, sends a 'poll_rodin_job_status' command to the Blender addon, and returns the result or an error message.
    @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)}"
  • The @mcp.tool() decorator registers the poll_rodin_job_status function as an MCP tool with the FastMCP server.
    @mcp.tool()
  • Function signature defines the input schema: optional subscription_key (str) for MAIN_SITE mode or request_id (str) for FAL_AI mode. The docstring provides detailed parameter descriptions and expected outputs.
    def poll_rodin_job_status(
        ctx: Context,
        subscription_key: str=None,
        request_id: str=None,
    ):
Behavior4/5

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

With no annotations provided, the description carries full burden and does well. It discloses this is a polling API, explains completion criteria for both modes, describes failure states, and provides behavioral context about when to proceed. It doesn't mention rate limits, authentication needs, or error handling details, but covers the essential operational behavior.

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 front-loaded with the core purpose. It efficiently organizes information by mode, though could be slightly more concise. Every sentence adds value: explaining parameters, return values, completion criteria, failure states, and polling behavior.

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?

For a tool with no annotations, no output schema, and 0% schema coverage, the description provides substantial context. It explains what the tool does, when to use it, parameter meanings, return value interpretation, and polling behavior. The main gap is lack of explicit error handling guidance, but overall it's quite complete given the complexity.

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?

With 0% schema description coverage for 2 parameters, the description fully compensates. It explains that 'subscription_key' is used for MAIN_SITE mode and 'request_id' for FAL_AI mode, specifying these come 'from the generate model step.' This adds crucial context beyond the bare parameter names in the schema.

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 (check/verify completion) 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 parameter requirements, and implicitly contrasts with other status-checking tools by focusing specifically on Rodin job completion.

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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