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 300s, prompted 45s.
`start_seconds`	Yes	The start time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 300s, 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.

Schema Changelog

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

Changed2 schema fields changedSep 2, 2026

changedInput schema / properties / end_seconds / description

Previous value: -"The end time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 180s, prompted 45s."New value: +"The end time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 300s, prompted 45s."

changedInput schema / properties / start_seconds / description

Previous value: -"The start time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 180s, prompted 45s."New value: +"The start time of the input audio in seconds. Maximum clip length depends on style.generation_mode: realistic 300s, prompted 45s."

First observedSep 1, 2026

TDQS

A4.5/5.0

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 discloses async execution, immediate id/credits_charged response, expected statuses, download URL availability, and the risk of hotlinked media URLs. This is exactly the contextual information an agent needs.

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 opening line is front-loaded and the guidance is organized into bullets, making the async workflow easy to parse. It is somewhat lengthy, but the complexity of the job and the absence of annotations justify nearly every sentence.

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 complex async tool with an output schema, the description covers the full invocation lifecycle: start, poll/wait, statuses, downloads, and file upload best practices. It loses a point because 'audio or text input' conflicts with the required audio_file_path; spelling out that text goes through style.prompt in prompted mode would remove the ambiguity.

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 coverage is 100%, so the baseline is 3; the description adds real value for *_file_path values by recommending Magic Hour paths or upload-URL file_paths and warning against unstable hotlinks. The phrase 'audio or text input' is slightly misleading because audio_file_path is required, but the schema itself clarifies the requirement.

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?

The description opens with a concrete verb-resource pair: 'Create a talking photo from an image and audio or text input.' This clearly identifies the tool's unique output and distinguishes it from image generation, video editing, and lip-sync sibling tools.

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 explicitly tells the agent what to do after the call: use wait_for_video_project with the returned id or poll GET /v1/video-projects/{id} until terminal statuses. It also gives practical file-path guidance with a fallback. It does not contrast this tool against sibling creation tools, so it stops short of a 5.

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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