Sceneplane

Wait for a job

job_wait

Read-onlyIdempotent

Long-poll a job until it reaches a terminal state or the timeout elapses. Default 90s, which is what a cold-starting Blender container usually needs; up to 300s is accepted but read the parameter note first, because a long single call trades round trips for connection risk. Cheaper than a job_get loop; chain calls for longer jobs, or register a webhook (webhooks_set) and stop polling altogether. Validation results include a complete aggregate report summary plus exact job_report continuations; result retains the compatible inline sample. Inspect report.inlineComplete and coverageComplete; truncated details or skipped checks never establish full coverage.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`jobId`	Yes	Durable job ID returned when an asynchronous operation is queued.
`timeoutSeconds`	No	Seconds to hold the connection open. 90 (default) is deliberately under the ~100s idle timeout common to HTTP proxies; values above that are allowed for direct connections but may return a transport error instead of a result. For jobs that genuinely take minutes, chain 90s calls or use webhooks_set.

Output Schema

TableJSON Schema

Name	Required	Description
`jobId`	Yes	Durable job ID returned when an asynchronous operation is queued.
`report`	Yes
`result`	Yes
`status`	Yes
`terminal`	Yes
`waitedMs`	Yes
`artifacts`	Yes
`errorCode`	Yes
`progressPct`	Yes
`errorMessage`	Yes
`statusMessage`	Yes
`resultRevisionId`	Yes
`resultArtifactIds`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 24, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior; the description adds meaningful extra context about connection risk, timeout tradeoffs, and the validation-result caveat. No contradiction with annotations.

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?

Core behavior and defaults are front-loaded, and every sentence contributes operational value. It is slightly dense with specialized clauses, but not padded.

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?

Covers behavior, default, limits, alternatives, risks, and key result conditions. With an output schema already present, nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and both parameters are already well documented there. The description adds the cold-start rationale for the 90s default but does not need to carry much parameter burden.

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?

States a specific verb and resource: long-poll a job until a terminal state or timeout. It also clearly differentiates itself from job_get loops and webhooks, so an agent can select it confidently.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says this is 'Cheaper than a job_get loop' and instructs chaining calls or using webhooks_set for longer jobs. This gives concrete when-to-use and when-not-to-use guidance.

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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