Skip to main content
Glama
opslon

BlenderMCP

by opslon

poll_hunyuan_job_status

Check the completion status of a Hunyuan3D generation task in BlenderMCP. Returns task progress and provides the generated 3D model file path when finished.

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

  • The handler function poll_hunyuan_job_status that polls the status of a Hunyuan3D job by calling a command via the blender connection.
    @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:
Behavior3/5

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 describes key traits: it's a polling API, returns statuses like 'DONE' or 'RUN', and includes the ZIP file path when done. However, it lacks details on error handling (e.g., failed states), rate limits, authentication needs, or whether it's read-only/destructive. This provides basic context but leaves gaps for a tool with no annotation support.

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 moderately concise but has some redundancy. It repeats information about the 'ResultFile3Ds' field in two sentences. The structure is front-loaded with the core purpose, but the later sentences could be streamlined. Overall, it's understandable but not optimally efficient.

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 (a polling tool for 3D generation), no annotations, no output schema, and 0% schema coverage, the description is somewhat complete. It covers the purpose, parameter, return values, and behavioral context like polling. However, it lacks details on error states, output structure beyond status, and integration with siblings like 'generate_hunyuan3d_model'. This makes it adequate but with clear gaps for effective agent use.

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 schema description coverage is 0%, so the description must compensate. It explicitly documents the single parameter 'job_id' and explains its purpose: 'The job_id given in the generate model step.' This adds meaningful semantics beyond the bare schema, clarifying where the job_id comes from. Since there's only one parameter, this is sufficient for a high score, though it doesn't cover format or constraints in detail.

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', which appears to serve a similar function, 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 on when to use the tool: 'This is a polling API, so only proceed if the status are finally determined.' It implies usage for checking completion status of a generation task, likely after invoking 'generate_hunyuan3d_model'. However, it doesn't explicitly state when not to use it or name alternatives like 'get_hunyuan3d_status', which could be a similar sibling tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/opslon/blender-mcp-optimized'

If you have feedback or need assistance with the MCP directory API, please join our Discord server