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poll_hunyuan_job_status

Check the completion status of a Hunyuan3D generation task in Blender, returning the 3D model file path when ready.

Instructions

Check if the Hunyuan3D generation task is completed.

For Hunyuan3D: Parameters: - job_id: The job_id given in the generate model step.

Returns the generation task status. The task is done if status is "DONE".
The task is in progress if status is "RUN".
If status is "DONE", returns ResultFile3Ds, which is the generated ZIP model path
When the status is "DONE", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.
This is a polling API, so only proceed if the status are finally determined ("DONE" or some failed state).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNo

Implementation Reference

  • MCP tool handler for polling Hunyuan3D job status. Forwards the job_id to Blender addon via send_command.
    @mcp.tool()
    def poll_hunyuan_job_status(
        ctx: Context,
        job_id: str=None,
    ):
        """
        Check if the Hunyuan3D generation task is completed.
    
        For Hunyuan3D:
            Parameters:
            - job_id: The job_id given in the generate model step.
    
            Returns the generation task status. The task is done if status is "DONE".
            The task is in progress if status is "RUN".
            If status is "DONE", returns ResultFile3Ds, which is the generated ZIP model path
            When the status is "DONE", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.
            This is a polling API, so only proceed if the status are finally determined ("DONE" or some failed state).
        """
        try:
            blender = get_blender_connection()
            kwargs = {
                "job_id": job_id,
            }
            result = blender.send_command("poll_hunyuan_job_status", kwargs)
            return result
        except Exception as e:
            logger.error(f"Error generating Hunyuan3D task: {str(e)}")
            return f"Error generating Hunyuan3D 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, returns statuses like 'DONE' or 'RUN', and includes the ResultFile3Ds field with a ZIP file path when done. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a polling tool.

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 has some redundancy. Sentences like 'Returns the generation task status...' and 'When the status is "DONE", the response includes...' repeat information about the ResultFile3Ds field. It could be more front-loaded and streamlined, but overall, it conveys necessary details without excessive verbosity.

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

Completeness3/5

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

Given the complexity of a polling tool with no annotations and no output schema, the description is moderately complete. It explains the purpose, parameter, return values, and behavioral context (polling nature). However, it lacks details on error states, response formats beyond statuses, or how to handle intermediate states, which could be important for robust agent usage.

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?

The input schema has 0% description coverage, so the description must compensate. It adds meaningful semantics: 'job_id: The job_id given in the generate model step.' This clarifies the parameter's purpose and origin, which is valuable beyond the schema's basic type information. Since there's only one parameter, this is sufficient for good understanding.

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 Hunyuan3D generation task is completed.' It specifies the verb ('check') and resource ('Hunyuan3D generation task'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hunyuan3d_status' or 'poll_rodin_job_status', which appear to serve similar polling functions for different job types.

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 usage: 'This is a polling API, so only proceed if the status are finally determined ("DONE" or some failed state).' This indicates when to use it—for checking final statuses—but doesn't explicitly state when not to use it or name alternatives like 'get_hunyuan3d_status' (a sibling tool), which might offer different functionality.

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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