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by ai.magichour.mcp
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AI GIF Generator

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
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

TableJSON 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. Changed1 schema field changed
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
-  "type": "string"
-}
  2. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
  3. First observed

TDQS

A3.9/5.0
Behavior5/5

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

Annotations only tell the agent this is a non-read-only, non-destructive, closed-world write. The description adds substantially beyond that: a concrete cost (50 credits per GIF), async job semantics, the immediate return payload (id plus credits_charged), terminal status values, and the fact that completed projects carry direct download URLs. This is rich behavioral context an agent needs before invoking a paid async operation.

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?

The core purpose and cost are front-loaded in the first two sentences, and the remaining MCP guidance is bulleted and scannable. It is slightly verbose, but nearly every clause (async, wait helper, statuses, downloads) carries distinct information, so little is wasted.

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?

Because an output schema exists, the description does not need to enumerate return fields, yet it helpfully explains the async lifecycle and the credits_charged handoff anyway. Combined with annotations covering the safety profile, the definition is nearly complete; only the missing sibling-comparison guidance keeps it from a 5.

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?

With 67% schema description coverage, the schema already documents name, style.prompt, and output_format, so the baseline is 3. The description adds no parameter-level meaning of its own (it only mentions the required style.prompt indirectly via credits/prompt context), so it neither compensates for the coverage gap nor falls below baseline.

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 opens with a specific verb+resource: 'Create an AI GIF.' This clearly distinguishes the tool from static-image siblings like ai_image_generator_create_image at the resource level. However, it never explicitly contrasts itself with those siblings or with animation_create_video, so the differentiation is implied rather than stated.

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?

It supplies a clear post-invocation workflow (call wait_for_image_project or poll the GET endpoint until status is complete/error/canceled), which is useful operational guidance. But it gives no guidance on when to choose this tool over ai_image_generator_create_image or other image-creation siblings, leaving tool selection to inference.

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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