Magic Hour

ai_gif_generator_create_image

Create an AI GIF. Each GIF costs 50 credits.

MCP guidance:

This starts an async image generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_image_project helper with the returned id, or poll the matching GET /v1/image-projects/{id} endpoint until status is complete, error, or canceled. Completed projects include downloads with direct URLs. The custom wait helper also returns exact_download_urls separately from expiration metadata.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No	Give your gif a custom name for easy identification.	Ai Gif - dateTime
`style`	Yes
`output_format`	No	The output file format for the generated animation.	gif

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique 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_charged`	Yes	The 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.

TDQS

A4.5/5.0

Behavior5/5

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

There are no annotations, so the description carries the full behavioral disclosure burden. It explicitly reveals that the tool starts an async job, returns id and credits_charged immediately, what terminal statuses exist, and how to retrieve the finished result and download URLs. This is strong transparency for a generation tool.

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 well-structured: a clear first sentence states the core action, and the MCP guidance bullet delivers the async workflow without unnecessary filler. Every sentence adds operational value.

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 async nature of the tool, the description covers the essential lifecycle: immediate return, credit charges, waiting/polling, terminal statuses, and download URLs. The presence of an output schema further reduces the need to document return fields, making this description complete for correct invocation and follow-up.

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 description coverage is 67%, and the schema already documents name, prompt, and output_format with meaningful descriptions. The description adds the credit-cost context but does not add extra semantics for the parameters themselves, so it neither fully compensates for uncovered schema gaps nor repeats the schema. A 3 is appropriate.

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 states a specific verb and resource: 'Create an AI GIF' followed by concrete detail on credit cost and async job behavior. It clearly distinguishes this tool from siblings like ai_image_generator_create_image by naming the output format (GIF) and the async generation model.

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?

The description gives clear post-invocation usage guidance: it explains that the call returns immediately, and tells the agent to call wait_for_image_project with the returned id or poll the endpoint. It does not explicitly compare against alternative creation tools, so it earns a 4 rather than a 5.

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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TDQS

A3.9/5.0

Disambiguation3/5

Most tools are differentiated by product-specific prefixes (e.g., lip_sync, text_to_video, image_upscaler), but the set contains many overlapping create_image/create_video tools, and generic editors like ai_image_editor_create_image and ai_video_editor_create_video blur boundaries with their more specific counterparts. Face/body swapping tools also occupy a similar conceptual space, requiring careful description reading to avoid misselection.

Naming Consistency4/5

Names generally follow a descriptive snake_case pattern of feature plus action (e.g., text_to_video_create_video, image_projects_delete, wait_for_image_project). Minor inconsistencies like ai_face_editor_edit_image versus the dominant create_image suffix, and the mixed ai_ prefix usage across tools, prevent a perfect score.

Tool Count2/5

44 tools is a large surface for an MCP server, even for a broad media-generation API. The count exceeds the 25+ threshold and creates a heavy selection burden, especially with over a dozen create tools for images and videos.

Completeness4/5

The surface covers the full create-to-download workflow for image, video, and audio: creation, status polling, wait helpers, fetch helpers, delete, and asset upload support. Minor gaps include no list/cancel endpoints and no general project search, but agents can complete core tasks without dead ends.

Resources

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