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poll_rodin_job_status

Monitor Hyper3D Rodin generation task completion status in BlenderMCP. Check if 3D model generation jobs are finished or failed before proceeding.

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` tool handler is defined in `src/blender_mcp/server.py` using the `@mcp.tool()` decorator. It interacts with the Blender connection to send a command to check the job status for either a `subscription_key` (MAIN_SITE) or a `request_id` (FAL_AI).
    @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?

No annotations provided, so description carries full burden. It successfully discloses the dual-mode behavior (MAIN_SITE vs FAL_AI), different return formats (list vs single status), terminal states ('Done', 'COMPLETED', 'Failed'), and the polling pattern requirement to wait for final states.

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?

Appropriately structured with clear mode-specific sections. Front-loads the core purpose. Slightly verbose due to necessary duplication between modes, but every sentence conveys essential behavioral or parametric information.

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?

No output schema exists, but the description thoroughly documents return values for both modes (list of statuses vs single status, specific string values indicating completion/failure). Given the complexity of handling two different APIs, the description provides sufficient context for correct invocation.

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, the description fully compensates by explaining both parameters (subscription_key, request_id) and their provenance ('given in the generate model step'). It also clarifies which parameter applies to which 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 opens with a specific verb ('Check') and resource ('Hyper3D Rodin generation task'). It clearly distinguishes this polling tool from generation siblings (generate_hyper3d_model_via_images/text) and from general status tools (get_hyper3d_status) by focusing on job/task completion checking.

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?

Explicitly states 'This is a polling API, so only proceed if the status are finally determined,' providing clear temporal guidance. References 'the generate model step' linking it to sibling generation tools, though it could explicitly name those tools as prerequisites.

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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