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by ai.magichour.mcp

text_to_video_create_video

What this API does

Create the same Text To Video you can make in the browser, but programmatically, so you can automate it, run it at scale, or connect it to your own app or workflow.

Good for

  • Automation and batch processing

  • Adding text to video into apps, pipelines, or tools

How it works (3 steps)

  1. Upload your inputs (video, image, or audio) with Generate Upload URLs and copy the file_path.

  2. Send a request to create a text to video job with the basic fields.

  3. Check the job status until it's complete, then download the result from downloads.

Key options

  • Inputs: see the request schema for endpoint-specific assets

  • Resolution: free users default to 480p; higher plans unlock HD and larger sizes

  • Extra fields: see the request schema for endpoint-specific options

Cost
Credits are only charged for the frames that actually render. You'll see an estimate when the job is queued, and the final total after it's done.

For detailed examples, see the product page.

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoGive your video a custom name for easy identification.Text To Video - dateTime
audioNoWhether to include audio in the video. Defaults to `false` if not specified. Audio support varies by model: * **`kling-2.6`**: Not supported * **`kling-3.0`**: Toggle-able: audio adds extra credits when enabled * **`ltx-2.3`**: Toggle-able: no additional credits for audio * **`minimax-h3`**: Toggle-able: no additional credits for audio * **`seedance-1.5`**: Toggle-able: audio adds extra credits when enabled * **`seedance-2.0`**: Toggle-able: no additional credits for audio * **`seedance-2.0-mini`**: Toggle-able: no additional credits for audio * **`seedance-2.5`**: Toggle-able: no additional credits for audio * **`sora-2`**: Toggle-able: no additional credits for audio * **`veo3.1`**: Toggle-able: audio adds extra credits when enabled * **`veo3.1-lite`**: Toggle-able: audio adds extra credits when enabled * **`wan-2.2`**: Not supported
modelNoThe AI model to use for video generation. * `default`: uses our currently recommended model for general use. For paid tiers, defaults to `kling-3.0`. For free tiers, it defaults to `ltx-2.3`. * `kling-2.6`: Great for action, motion blur, and camera moves. * `kling-3.0`: Best overall quality for cinematic storytelling. * `ltx-2.3`: Fastest output. Best for rapid iteration. * `minimax-h3`: Reference-driven video with native audio. * `seedance-1.5`: Smooth, consistent motion with precision. * `seedance-2.0`: Top quality with reference-to-video control. * `seedance-2.0-mini`: Fast, consistent video with strong motion quality * `seedance-2.5`: Highest quality with superior realism, detail, and motion * `sora-2`: Open AI's model. Great for creativity and viral clips. * `veo3.1`: Google's model. Highest realism and detail. * `veo3.1-lite`: Veo quality at a more accessible cost. * `wan-2.2`: Strong physics, camera moves, and motion. If you specify the deprecated model value that includes the `-audio` suffix, this will be the same as included `audio` as `true`.default
styleYes
resolutionNoControls the output video resolution. Defaults to `720p` on paid tiers and `480p` on free tiers. * **`kling-2.6`**: Supports 720p, 1080p. * **`kling-3.0`**: Supports 720p, 1080p, 4k. * **`ltx-2.3`**: Supports 480p, 720p, 1080p. * **`minimax-h3`**: Supports 480p, 720p, 1080p. * **`seedance-1.5`**: Supports 480p, 720p, 1080p. * **`seedance-2.0`**: Supports 480p, 720p. * **`seedance-2.0-mini`**: Supports 480p, 720p. * **`seedance-2.5`**: Supports 480p, 720p. * **`sora-2`**: Supports 720p. * **`veo3.1`**: Supports 720p, 1080p. * **`veo3.1-lite`**: Supports 720p, 1080p. * **`wan-2.2`**: Supports 480p, 720p, 1080p.
end_secondsYesThe total duration of the output video in seconds. Supported durations depend on the chosen model: * **`kling-2.6`**: 5, 10 * **`kling-3.0`**: 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`ltx-2.3`**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 * **`minimax-h3`**: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 * **`seedance-1.5`**: 4, 5, 6, 7, 8, 9, 10, 11, 12 * **`seedance-2.0`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`seedance-2.0-mini`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 * **`seedance-2.5`**: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 * **`sora-2`**: 4, 8, 12, 24, 36, 48, 60 * **`veo3.1`**: 4, 6, 8, 16, 24, 32, 40, 48, 56 * **`veo3.1-lite`**: 4, 6, 8, 16, 24, 32, 40, 48, 56 * **`wan-2.2`**: 3, 4, 5, 6, 7, 8, 9, 10, 15
aspect_ratioNoDetermines the aspect ratio of the output video. * **`kling-2.6`**: Supports 9:16, 16:9, 1:1. * **`kling-3.0`**: Supports 9:16, 16:9, 1:1. * **`ltx-2.3`**: Supports 9:16, 16:9, 1:1. * **`minimax-h3`**: Supports 16:9, 9:16, 1:1. * **`seedance-1.5`**: Supports 9:16, 16:9, 1:1. * **`seedance-2.0`**: Supports 9:16, 16:9, 1:1. * **`seedance-2.0-mini`**: Supports 9:16, 16:9, 1:1. * **`seedance-2.5`**: Supports 9:16, 16:9, 1:1. * **`sora-2`**: Supports 9:16, 16:9. * **`veo3.1`**: Supports 9:16, 16:9. * **`veo3.1-lite`**: Supports 9:16, 16:9. * **`wan-2.2`**: Supports 9:16, 16:9, 1:1.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique 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_chargedYesThe 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

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers richly. It discloses the async job nature ('starts an async video generation job and returns id plus credits_charged immediately'), status lifecycle ('complete', 'error', 'canceled'), the downloads field with direct URLs, the exact_download_urls detail from the wait helper, and the credits-only-for-rendered-frames cost model. This fully explains what the agent will experience when invoking the tool.

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 long but well-structured with clear headers (What this API does, Good for, How it works, Key options, Cost, MCP guidance), and the core purpose is front-loaded in the first sentence. Each section earns its place given the tool's complexity (async behavior, model-specific constraints, cost implications). The MCP guidance section is particularly valuable for agent decisions. It could be slightly tightened, but the structure makes the length navigable.

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?

Given the tool's complexity — 7 parameters, nested objects, async execution, and model-specific constraints — the description covers the essential workflow end-to-end: upload inputs via presigned URLs, create the job, poll or wait for completion, and download from the result. Cost expectations are disclosed, resolution tier behavior is explained, and since an output schema exists, the return structure needn't be duplicated. Nothing critical is missing.

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?

Schema description coverage is 86% (high, above the 80% baseline threshold), so the schema already documents the parameters thoroughly. The description adds minor context beyond the schema, such as free-tier resolution defaulting to 480p and credits being charged per rendered frame, but it defers parameter details to 'see the request schema.' This matches the baseline-3 expectation for high-coverage schemas.

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 specific verb+resource: 'Create the same Text To Video you can make in the browser, but programmatically.' It clearly distinguishes this tool from siblings like image_to_video_create_video and video_to_video_create_video by naming the input modality (text) explicitly. The 'Good for' section reinforces what makes this tool unique: automation, batch processing, and app integration.

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 'Good for' section gives clear use-case context (automation, batch processing, embedding in apps/pipelines), and the MCP guidance explicitly tells agents what to do for async completion: call wait_for_video_project or poll the matching endpoint. However, it does not explicitly say when NOT to use this tool versus text-to-image or video-to-video alternatives, leaving some routing to inference from the name.

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