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Create GIF or short video

create_gif_or_short_video

Generates a short animation from a text prompt, with GIF, MP4 or WebM output. Costs 50 credits. Starts a background job; returns a project id for wait_for_image_project.

Input Schema

NameRequiredDescriptionDefault
nameNoGive your gif a custom name for easy identification.Ai Gif - dateTime
styleYes
output_formatNoThe output file format for the generated animation.gif

Output Schema

NameRequiredDescriptionDefault
idYesUnique ID of the image. Use it with the [Get image Project API](https://docs.magichour.ai/api-reference/image-projects/get-image-details) to fetch status and downloads.
credits_chargedYesThe amount of credits deducted from your account to generate the image. We charge credits right when the request is made. If an error occurred while generating the image(s), credits will be refunded and this field will be updated to include the refund.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly=false and destructive=false, so the bar is lower, and the description still adds real behavioral context: a fixed 50-credit cost and asynchronous background-job semantics. The non-obvious detail that the job is awaited via wait_for_image_project rather than wait_for_video_project is exactly the kind of trap this field should catch; only refund/failure behavior is missing.

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?

Three short clauses with zero filler: capability first, cost second, async/return contract last. Everything an agent needs to act is front-loaded.

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?

With an output schema present, return-value explanation is optional, yet the description still supplies the key chaining fact (project id → wait tool) plus cost. The nested style.prompt object and the sibling-overlap question are the only remaining gaps for a paid async tool.

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 67%, and the schema already documents prompt (max 500), output_format enum, and the name default. The description restates the three formats and the text-prompt nature without adding format-level meaning such as aspect ratio, duration, or motion guidance, so it is baseline rather than compensatory.

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 states a specific verb and resource — generating a short animation from a text prompt — plus the concrete outputs (GIF, MP4, WebM). It is clear on its own, but it never distinguishes itself from close siblings like create_animation or create_video_from_text, so an agent cannot route between them from this text alone.

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

Usage Guidelines3/5

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

Usage is implied by the output-format scope, and the description does supply a workflow pointer ('returns a project id for wait_for_image_project'), which is genuinely helpful. However, it gives no when-to-use vs when-not guidance against the many other animation/video-from-text siblings.

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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