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blender-lab-mcp-client

by A-to-PC

blender_job_status

Check the status of an asynchronous Blender job by its job ID. Returns queued, running, succeeded, failed, or cancelled states with timestamps, output, and errors to track progress after launching async Python execution.

Instructions

Get the status of an async Blender job. Returns job_id, status (queued/running/succeeded/failed/cancelled), timestamps, result, stdout, stderr, and error. Poll this after starting a job with blender_python_exec_async.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavior disclosure. It explains that the call returns status values, timestamps, result, stdout, stderr, and error, and that it is a polling call after an async launch. It could more explicitly say it is read-only and does not modify the job, but the wording makes that reasonably clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is three concise sentences: the action, the return shape, and the use trigger. Every sentence adds information, and the most important guidance appears early.

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?

For a one-parameter status tool with an output schema, the description covers the parameter, the return contents, and the intended polling scenario. It is complete enough for correct invocation, though it would benefit from an explicit note about read-only behavior and job-result persistence.

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 only labels the parameter as 'Job Id', so the description adds useful meaning by tying job_id to an async job started by blender_python_exec_async. It does not explicitly state that job_id is returned from that tool, but the relationship is strongly implied and sufficient for a single-parameter tool.

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 uses a specific verb ('Get') and resource ('async Blender job'), and it clearly distinguishes this from sibling tools by addressing a single job's status rather than listing or canceling jobs. The link to blender_python_exec_async makes the intent unambiguous.

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?

It explicitly states when to use the tool: poll after starting a job with blender_python_exec_async. It does not explicitly exclude alternatives or name a sibling like blender_job_list, but the guidance is clear and actionable.

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