Magic Hour

by ai.magichour.mcp

audio_projects_retrieve_details

Check the progress of a audio 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 audio project. This value is returned by all of the POST APIs that create an audio.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	Unique ID of the audio. Use it with the [Get audio Project API](https://docs.magichour.ai/api-reference/audio-projects/get-audio-details) to fetch status and downloads.
`name`	Yes	The name of the audio.
`type`	Yes	The type of the audio project. Possible values are VOICE_GENERATOR, VOICE_CHANGER, VOICE_CLONER, VIDEO_TO_AUDIO, MUSIC_GENERATOR
`error`	Yes	In the case of an error, this object will contain the error encountered during video render
`status`	Yes	The status of the audio. - `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 `audio.started` webhook event fires when rendering begins - `complete` - the job finished successfully; fires `audio.completed` - `error` - the job failed during processing; fires `audio.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
`credits_charged`	Yes	The amount of credits deducted from your account to generate the audio. We charge credits right when the request is made. If an error occurred while generating the audio, credits will be refunded and this field will be updated to include the refund.

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so well: it lists all statuses, notes that downloads only exist after a successful render, and warns that signed URLs must be used exactly as returned. This is substantial, actionable behavioral context.

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?

Purpose is front-loaded, followed by a compact status list and focused MCP guidance. Every section adds value with no redundant filler.

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 one-parameter retrieval tool with an output schema, this is nearly complete: it covers polling usage, status semantics, and download URL handling. It could more explicitly distinguish itself from wait_for_audio_project, but the guidance is already actionable.

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 schema already covers the single id parameter 100%, including that it is returned by the POST APIs. The description reinforces that the id comes from a create call but does not add new parameter-level meaning beyond that.

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?

States a specific action: 'Check the progress of a audio project' and provides a concrete status taxonomy. The verb and resource make it easy to distinguish from create, delete, and wait tools despite not naming them explicitly.

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?

Explicitly tells the agent to use this after a create tool to poll job status and maps statuses to user-facing actions. It does not name exclusions or the wait_for_audio_project alternative, so routing between those siblings is left somewhat implicit.

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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