Magic Hour

ai_talking_photo_create_talking_photo

Create a talking photo from an image and audio or text input.

MCP guidance:

This starts an async video generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_video_project helper with the returned id, or poll the matching GET /v1/video-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.	Talking Photo - dateTime
`style`	No	Attributes used to dictate the style of the output
`assets`	Yes	Provide the assets for creating a talking photo
`end_seconds`	Yes	The end time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 180s, prompted 45s.
`start_seconds`	Yes	The start time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 180s, prompted 45s.
`max_resolution`	No	Constrains the larger dimension (height or width) of the output video. Allows you to set a lower resolution than your plan's maximum if desired. The value is capped by your plan's max resolution.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique ID of the video. Use it with the [Get video Project API](https://docs.magichour.ai/api-reference/video-projects/get-video-details) to fetch status and downloads.
`credits_charged`	Yes	The amount of credits deducted from your account to generate the video. If the status is not 'complete', this value is an estimate and may be adjusted upon completion based on the actual FPS of the output video. If video generation fails, credits will be refunded, and this field will be updated to include the refund.

TDQS

A4/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly explains that this is an async video generation job that returns id and credits_charged immediately, requires polling or waiting, and yields downloads with direct URLs. It also warns about flaky hotlinked URLs. This is strong transparency, though it does not mention auth requirements, rate limits, or potential failure reasons beyond statuses.

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 description is well-structured: a one-sentence summary followed by focused MCP guidance paragraphs. The async workflow and file-path guidance are dense with actionable information and contain no filler. It is slightly long relative to the simple summary, but every sentence earns its place in explaining the correct invocation flow.

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?

Given the nested asset schema and async behavior, the description covers the critical operational details: immediate job response, waiting/polling, completion statuses, downloads, and file upload strategy. The output schema exists, so return values need no explanation. Minor gaps remain, such as clarifying that text input is only via style.prompt and not a text-only alternative to audio, and there is no mention of authentication or cost implications beyond credits_charged being returned.

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?

The schema already has 100% coverage of parameters, so the baseline is 3. The description adds value beyond that by explaining how to source *_file_path values: prefer an existing Magic Hour file path or the file_path returned by the upload-URL endpoint, with warnings about hotlinked URLs. This practical guidance helps the agent format key parameters correctly, pushing the score above 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 clearly states the tool creates a talking photo from an image plus audio or text input, which is a specific verb+resource pair. It is distinguishable from siblings like image_to_video or lip_sync, though it does not explicitly name or contrast those alternatives. The phrase 'audio or text input' is slightly misleading because the schema requires audio_file_path and text is only an optional style.prompt field, but the overall purpose is unambiguous.

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 MCP guidance provides explicit procedural context: it tells the agent to call wait_for_video_project or poll the GET endpoint to get the final result, and specifies the statuses to watch for. It also gives conditional guidance on file paths, such as preferring Magic Hour file paths and using the presigned upload flow when unsure. However, it does not compare this tool against alternatives like lip_sync_create_video or audio_to_video_create_video, so the when-to-use-versus-sibling guidance is incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Magic Hour tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?