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BlenderMCP

poll_rodin_job_status

Monitor Hyper3D Rodin generation task completion by checking status for subscription keys or request IDs. Use to determine when 3D models are ready for Blender integration.

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 sends a command to the Blender addon to poll the status of a Rodin job using either a subscription_key (MAIN_SITE mode) or request_id (FAL_AI mode). The function signature and docstring define the input schema.
    @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)}"
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 key traits: it's a polling API, explains what constitutes completion ('Done' or 'COMPLETED'), failure conditions ('Failed' or other statuses), and progress states ('IN_PROGRESS'). It also clarifies that users should wait for final states before proceeding. This covers essential behavioral aspects without contradictions, though it lacks details on 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.

Conciseness3/5

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

The description is appropriately sized but could be more front-loaded. It starts with a clear purpose, but the detailed breakdown into two modes (MAIN_SITE and FAL_AI) is somewhat repetitive and could be streamlined. Every sentence adds value, but the structure feels slightly verbose, with redundant phrases like 'This is a polling API' repeated for each mode.

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 (two operational modes, no annotations, no output schema), the description is largely complete. It explains the tool's purpose, usage, parameters, and expected behaviors (polling, status interpretation). However, it doesn't detail the return format (e.g., structure of the 'list of status' or specific failed states), which could be helpful since there's no output schema. This minor gap prevents a perfect score.

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?

Schema description coverage is 0%, so the description must compensate fully. It does so by clearly explaining the semantics of both parameters: 'subscription_key' for MAIN_SITE mode and 'request_id' for FAL_AI mode, linking them to previous steps ('given in the generate model step'). This adds crucial meaning beyond the bare schema, ensuring users understand when and how to use each parameter effectively.

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's purpose: 'Check if the Hyper3D Rodin generation task is completed.' It specifies the verb ('check') and resource ('Hyper3D Rodin generation task'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hyper3d_status', which might serve a similar purpose, preventing a perfect score.

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?

The description provides clear context for when to use this tool: it's a polling API to check job status, and it advises to 'only proceed if the status are finally determined.' It also implicitly distinguishes usage between two modes (MAIN_SITE and FAL_AI) based on parameters. However, it doesn't explicitly mention when not to use it or name alternatives among siblings, such as 'get_hyper3d_status', which could be a related tool.

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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