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goodfy704

AI-video-generator-MCP

by goodfy704

Create Video

create_video

Generate a video from a text prompt, returning a job ID for tracking. Poll the status until it shows completed, then retrieve the finished video.

Instructions

Start generating a video from a text prompt.

Returns immediately with a job_id; the video is not ready yet. Poll get_video_status with that job_id until the status is 'completed', then call get_video_result to retrieve the video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate.
durationNoLength in seconds, from 1 to 60.
aspect_ratioNoOne of '16:9', '9:16', '1:1', '4:3', '21:9'.16:9

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

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of explaining behavior. It clearly discloses the asynchronous nature ('Returns immediately with a job_id; the video is not ready yet') and directs the agent to poll for completion. It does not mention failure modes or cancellation, but the core non-obvious behavior is covered.

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 sentences with zero redundancy. It front-loads the purpose, then gives the async caveat and next steps in order. Every sentence earns its place.

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?

Given the output schema exists and the async workflow is described, the description is complete. It tells the agent exactly what to do after calling this tool, including which siblings to invoke and when. Nothing essential 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%, so the baseline is 3. The description mentions 'text prompt' but does not add meaning beyond what the schema already provides for the three parameters. It adds no new semantic context for duration or aspect_ratio.

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 opens with a specific verb and resource: 'Start generating a video from a text prompt.' It clearly distinguishes this tool from its siblings by framing it as the submission step, while the follow-up tools are explicitly named for later stages.

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?

The description provides an explicit workflow: call this tool to start generation, then poll get_video_status until 'completed', then call get_video_result. This directly tells the agent how to use the tool versus the alternatives, leaving no ambiguity about the lifecycle.

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