Magic Hour

by ai.magichour.mcp

image_projects_retrieve_details

Check the progress of a image project. The downloads field is populated after a successful render.

Statuses

queued — waiting to start
rendering — in progress
complete — ready; see downloads
error — a failure occurred (see error)
canceled — user canceled
draft — not used

MCP guidance:

Use this after a create tool to poll job status. When status is complete, surface the downloads URLs to the user; if status is error, surface the error message.
Each downloads[n].url is already the full signed download URL. Use it exactly as returned. Do not shorten it, strip query parameters, or append expires_at onto the URL string.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique ID of the image project. This value is returned by all of the POST APIs that create an image.

Output Schema

TableJSON Schema

Name	Required	Description
`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.
`name`	Yes	The name of the image.
`type`	Yes	The type of the image project. Possible values are FACE_EDITOR, AI_IMAGE_EDITOR, AI_SELFIE, AI_HEADSHOT, AI_INFLUENCER, AI_IMAGE, AI_MEME, CLOTHES_CHANGER, BACKGROUND_REMOVER, FACE_SWAP, IMAGE_UPSCALER, IMAGE_ENHANCER, AI_GIF, QR_CODE, PHOTO_EDITOR, PHOTO_COLORIZER, IMAGE_COLOR_GRADER, HEAD_SWAP, BODY_SWAP, STORYBOARD, IMAGE_EXPANDER
`error`	Yes	In the case of an error, this object will contain the error encountered during video render
`status`	Yes	The status of the image. - `draft` - the project was created but has not been submitted for rendering - `queued` - the job is waiting for an available server - `rendering` - the job is being processed; the `image.started` webhook event fires when rendering begins - `complete` - the job finished successfully; fires `image.completed` - `error` - the job failed during processing; fires `image.errored` - `canceled` - the job was manually canceled (for example from the Magic Hour web app) Note: `rendering`, `complete`, and `error` have matching webhook events; `canceled` does not - a canceled job emits no webhook event, so poll this endpoint to detect cancellation.
`enabled`	Yes	Whether this resource is active. If false, it is deleted.
`downloads`	Yes
`created_at`	Yes
`image_count`	Yes	Number of images generated
`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 5, 2026

changedOutput schema / properties / type / description

Previous value: -"The type of the image project. Possible values are FACE_EDITOR, AI_IMAGE_EDITOR, AI_SELFIE, AI_HEADSHOT, AI_INFLUENCER, AI_IMAGE, AI_MEME, CLOTHES_CHANGER, BACKGROUND_REMOVER, FACE_SWAP, IMAGE_UPSCALER, IMAGE_ENHANCER, AI_GIF, QR_CODE, PHOTO_EDITOR, PHOTO_COLORIZER, HEAD_SWAP, BODY_SWAP, STORYBOARD, IMAGE_EXPANDER"New value: +"The type of the image project. Possible values are FACE_EDITOR, AI_IMAGE_EDITOR, AI_SELFIE, AI_HEADSHOT, AI_INFLUENCER, AI_IMAGE, AI_MEME, CLOTHES_CHANGER, BACKGROUND_REMOVER, FACE_SWAP, IMAGE_UPSCALER, IMAGE_ENHANCER, AI_GIF, QR_CODE, PHOTO_EDITOR, PHOTO_COLORIZER, IMAGE_COLOR_GRADER, HEAD_SWAP, BODY_SWAP, STORYBOARD, IMAGE_EXPANDER"

First observedSep 1, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does a good job: it discloses that downloads appears only after a successful render, enumerates all statuses, and warns that download URLs are signed and must be used verbatim. It does not discuss authentication or rate limits, but those are less central for a status retrieval.

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 well structured: a one-line purpose, a compact status list, and a practical MCP guidance block. It front-loads the primary action and each sentence adds operational value.

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 a status-polling tool, it covers lifecycle statuses, the downloads field, the signed URL caveat, and next actions on completion/error. It lacks an explicit pointer to the wait_for_image_project sibling for blocking waits, but is otherwise complete for a tool with an output schema.

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?

The single id parameter is already fully documented in the schema, including that POST APIs return it, and schema coverage is 100%. The description adds no extra parameter nuance, so it earns the 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 first sentence names a specific operation: checking the progress of an image project, with statuses and downloads giving the agent a concrete picture. It is clear enough to identify as the details/status tool, though it does not explicitly contrast with the sibling wait_for_image_project.

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?

It explicitly says to use the tool after a create call to poll job status and gives follow-up actions for complete and error. It does not state when to prefer wait_for_image_project, so it stops short of a full when-to-use/when-not-to-use matrix.

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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