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by ai.magichour.mcp
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AI GIF Generator

ai_gif_generator_create_image

MCP guidance:

  • Starts an async image job; returns id and credits_charged. For finished results, call wait_for_image_project with that id or poll GET /v1/image-projects/{id} until complete, error, or canceled. Results include downloads; wait helpers add exact_download_urls. Preserve signed URLs and query parameters; never append expiration metadata.

Create an AI GIF. Each GIF costs 50 credits.

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. 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

A4.1/5.0
Behavior5/5

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

Annotations only declare the safety profile (readOnly=false, destructive=false, openWorld=false); the description goes well beyond that by disclosing async job semantics, the returned id and credits_charged, the terminal states, the 50-credit cost per GIF, and the signed-URL preservation constraint. This is exactly the kind of operational context annotations cannot carry.

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?

Front-loaded with the lifecycle guidance an agent needs before calling, followed by the one-line purpose and cost. The "MCP guidance:" prefix and dense URL-preservation sentence are slightly noisy, but overall it is tight and earns its length.

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 an async creation tool with a rich output schema, the description covers the full lifecycle, cost, and result handling. Remaining gaps (what triggers a failure beyond the state names, whether the job can be canceled explicitly) are minor given the output schema exists.

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 every parameter (name, style.prompt, output_format) already has an inline description and enum. The description adds nothing parameter-specific, so the schema does the heavy lifting and a baseline 3 is appropriate.

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?

"Create an AI GIF" is a specific verb+resource pair that clearly identifies the operation. It does not, however, explicitly differentiate itself from adjacent siblings such as ai_image_generator_create_image or ai_meme_generator_create_image, so an agent must infer the distinction from the name alone.

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 guidance block gives concrete follow-up routing: call wait_for_image_project with the returned id, or poll GET /v1/image-projects/{id} until complete/error/canceled. That is strong procedural context, but there is no explicit statement of when to prefer this GIF tool over the other image-creation 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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