Magic Hour

ai_clothes_changer_create_image

Change outfits in photos in seconds with just a photo reference. Each photo costs 25 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.
For *_file_path values, prefer an existing Magic Hour file path or a file_path returned by the upload-URL endpoint after the file bytes are uploaded. Direct public media URLs may work when they are stable, fetchable, and return raw file bytes, but hotlinked URLs can fail; when in doubt, use the presigned upload flow first and pass the returned file_path.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No	Give your image a custom name for easy identification.	Clothes Changer - dateTime
`assets`	Yes	Provide the assets for clothes changer

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 2, 2026

changedInput schema / properties / assets / properties / garment_type / description

Previous value: -"Type of garment to swap. If not provided, swaps the entire outfit. \n* `upper_body` - for shirts/jackets \n* `lower_body` - for pants/skirts \n* `dresses` - for entire outfit (deprecated, use `entire_outfit` instead) \n* `entire_outfit` - for entire outfit"New value: +"Type of clothing item to swap. If not provided, swaps the entire outfit. \n* `upper_body` - for shirts/jackets \n* `lower_body` - for pants/skirts \n* `dresses` - for entire outfit (deprecated, use `entire_outfit` instead) \n* `entire_outfit` - for entire outfit"

First observedSep 1, 2026

TDQS

A4.2/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well. It discloses that the operation is async, returns id and credits_charged immediately, requires polling or a wait helper, lists terminal statuses, and notes that hotlinked URLs can fail. This is far more than a typical 'does something' description.

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 front-loaded with the purpose and credit cost, followed by two tightly scoped bullets covering async flow and file-path best practices. Every sentence adds distinct value without repetition.

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 tool's complexity—async generation, credit cost, file uploads, and result retrieval—the description covers the full lifecycle: starting the job, checking status, fetching downloads, and handling input files. The presence of an output schema further reduces the need to describe return shapes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is strong. The description adds meaningful guidance for *_file_path parameters: prefer Magic Hour file paths or file_path from the upload endpoint, be cautious with direct public URLs, and use the presigned upload flow when in doubt. This goes beyond the schema's static descriptions.

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 action, 'Change outfits in photos in seconds', which clearly identifies the tool's core function and resource. It is easily distinguishable from siblings like body_swap or face_swap, though it does not explicitly name a differentiating sibling.

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?

The MCP guidance explains how to handle the async job and how to prefer Magic Hour file paths versus direct URLs, which is useful operational context. However, there is no explicit statement about when to use this tool versus alternatives like body_swap_create_image or face_swap_photo_create_image.

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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