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goodfy704

AI-video-generator-MCP

by goodfy704

Get Video Status

get_video_status

Check the progress of a video generation job with its job ID. Determine if it is queued, running, completed, or cancelled.

Instructions

Check the progress of a video-generation job.

Use the job_id returned by create_video. Statuses are 'queued', 'running', 'completed' and 'cancelled'. Keep polling while the job is queued or running.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesIdentifier returned by create_video.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesIdentifier used to track this job.
promptYesPrompt the job was created from.
statusYesCurrent lifecycle state of the job.
messageYesHuman-readable summary of the job state.
durationYesRequested video length in seconds.
progressYesCompletion percentage, 0-100.
aspect_ratioYesRequested aspect ratio.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses the polling nature of the tool and lists all possible statuses, which is meaningful behavioral context beyond the tool name. It does not mention error behavior or rate limits, but for a read-only status check this is acceptable.

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 compact and efficiently structured: purpose first, followed by job_id source, status vocabulary, and polling guidance. Every sentence earns its place and there is no redundancy.

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 simple one-parameter tool with an output schema present, the description covers the essential behavior and polling loop. It would be slightly more complete if it explicitly mentioned using get_video_result once the status becomes 'completed', but this is reasonably inferable.

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?

The input schema already fully describes job_id with 100% coverage, so the baseline is 3. The description restates that job_id comes from create_video, matching the schema without adding new semantic 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 action ('Check the progress'), targets a specific resource ('video-generation job'), and enumerates the statuses. It differentiates itself from create_video and cancel_video, though it does not explicitly contrast with get_video_result.

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 provides concrete usage guidance: use the job_id from create_video and keep polling while status is queued or running. It does not explicitly state when to switch to get_video_result, but the polling instruction makes the intended usage clear.

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