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ai_talking_photo_create_talking_photo

Create a talking photo by animating an image with audio or a text prompt. Submit assets and timing, get a job ID to poll for the finished video download.

Instructions

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.

First observedv0.1.0Sep 23, 2026

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 fully discloses the async nature, immediate response fields, polling statuses, completion downloads, and exact_download_urls behavior. It also warns about hotlinked URL fragility and points to the presigned upload path, which is essential risk-relevant 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?

The description is dense but well-structured: one-purpose opening sentence, async lifecycle guidance, then file-path handling. The MCP guidance section is front-loaded with the most consequential behavior (async + wait helper) and every sentence delivers actionable information.

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?

For a complex async tool with no annotations, the description covers the full invocation lifecycle: what is returned, how to wait/poll, terminal statuses, download URLs, and file path sourcing. Output schema exists, so return-value documentation is not required, and nothing needed to call the tool correctly is missing.

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?

Input schema covers 100% of parameters, so baseline is 3. The description adds meaningful file-path semantics beyond the schema by distinguishing Magic Hour file paths, upload-URL-returned file_paths, and direct public URLs, which directly affects parameter correctness.

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 concrete operation—create a talking photo from an image plus audio—clearly separating it from siblings like text_to_video or image_to_video. However, the phrase 'audio or text input' is slightly misleading because the schema requires audio_file_path and no separate text input parameter exists; text only appears as an optional style prompt.

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?

Provides explicit post-invocation guidance: return of id/credits_charged, how to get finished result via wait_for_video_project or polling, and statuses to watch. It does not compare this tool against alternative creation tools, but the workflow guidance is clear enough for an agent to act correctly.

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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