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mcp-for-blender

by nludd25
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

poll_hunyuan_job_status

Check whether a Hunyuan3D generation task has finished. Retrieve downloadable model URLs (prefer .glb) once the task reaches DONE status.

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 with one or more downloadable model URLs.
    Prefer a .glb URL when present (self-contained with materials); otherwise use a .zip/.obj asset URL.
    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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description fully explains the polling behavior: statuses RUN/DONE/failure, and that a DONE status yields ResultFile3Ds with download URLs. It also gives asset-selection guidance (.glb preferred). It does not mention rate limits or auth, but the operation is inherently a read-only check, and the behavior is well-disclosed.

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 organized with a clear purpose statement, parameter guidance, return semantics, and asset-selection advice. It is somewhat longer than strictly necessary, but every sentence adds value and it is front-loaded with the core purpose.

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

Completeness5/5

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

For a polling tool with no output schema, the description covers everything an agent needs: how to identify completion, the meaning of each status, the return payload shape, and the preferred URL type. The lack of an output schema is fully compensated.

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?

The input schema has only a title and default for job_id, with 0% coverage. The description compensates by stating the parameter comes from the generate model step, which is essential context for supplying the correct value. This fully clarifies an otherwise opaque parameter.

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 states a specific verb ('Check if the Hunyuan3D generation task is completed') and resource (Hunyuan3D job). It clearly differentiates from the sibling poll_rodin_job_status by name and content, and ties to generate_hunyuan3d_model via the referenced job_id.

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?

Provides clear context: the job_id comes from the generation step and it is a polling API to be used until a final status. It does not explicitly say when NOT to use it versus poll_rodin_job_status, but the vendor-specific wording and name make the distinction obvious.

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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