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Get a job's status

get_job_status
Read-only

Check a video or audio job's status, progress percentage, and terminal outcome—done, failed, rejected, or moderation failure—to track completion and refunds.

Instructions

Status of a video job or audio clip: status, status_percentage, status_failed, credits_refunded. Terminal states: done; failed*, rejected_* and not_pass_moderation (audio) are failures. Prefer wait_for_job, which polls for you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
modelYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover safety (readOnly/openWorld), and the description earns credit by disclosing terminal-state semantics: done is success, while failed*, rejected_* and not_pass_moderation are failures, plus the credits_refunded signal. It doesn't say how long results are retained or what a non-terminal percentage means.

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?

Compact and front-loaded: what it returns first, then failure semantics, then the recommended alternative. The telegraphic style ('failed*', 'rejected_*') is dense but efficient; the asterisk notation is mildly ambiguous.

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?

With no output schema, listing the return fields and failure states is genuinely necessary and present. However, for a 2-parameter tool with 0% schema coverage, the complete silence on how to supply model/id leaves a real gap for correct invocation.

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

Parameters2/5

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

Schema coverage is 0%, so the description carries the full burden for model and id, yet neither is mentioned. The phrase 'a video job or audio clip' only loosely hints at the model enum values without explaining that model must match the original creation call.

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?

Names a specific verb+resource and enumerates the returned status fields (status, status_percentage, status_failed, credits_refunded), which clearly separates it from a result-fetching tool. It stops short of explicitly distinguishing itself from the sibling get_job_result, so an agent still has to infer that division of labor.

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 names an alternative and the condition that selects it: prefer wait_for_job because it polls for you. It gives no when-not guidance (e.g., use this instead for a single non-blocking check) and doesn't mention get_job_result.

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