Magic Hour

image_background_remover_create_image

Remove background from image. Each image costs 5 credits.

MCP guidance:

This starts an async image generation job and returns id plus credits_charged immediately. If the user wants the finished result, call the wait_for_image_project helper with the returned id, or poll the matching GET /v1/image-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.	Background Remover - dateTime
`assets`	Yes	Provide the assets for background removal

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Unique ID of the image. Use it with the [Get image Project API](https://docs.magichour.ai/api-reference/image-projects/get-image-details) to fetch status and downloads.
`credits_charged`	Yes	The amount of credits deducted from your account to generate the image. We charge credits right when the request is made. If an error occurred while generating the image(s), credits will be refunded and this field will be updated to include the refund.

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains async execution, immediate return of id and credits_charged, required follow-up polling, terminal statuses, download URLs, the wait helper's exact_download_urls behavior, and the failure risk of hotlinked URLs. This is rich, non-obvious context that the schema cannot convey.

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 front-loaded with the one-sentence purpose and cost, then organized into two focused MCP-guidance bullets. It is longer than a minimal description, but every sentence carries operational information an agent needs; there is no filler or duplication of schema content.

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 an async image-creation tool with nested required assets and a supporting wait helper, the description covers the full lifecycle: cost, immediate response, follow-up, terminal statuses, download retrieval, and file-path sourcing. Since an output schema exists, restating return values is unnecessary. The guidance is complete enough for an agent to invoke and complete the task correctly.

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 input schema already documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful guidance beyond the schema: prefer existing Magic Hour file paths or upload-URL file_paths, note that direct public URLs may work but hotlinks can fail, and use the presigned upload flow when uncertain. This materially helps an agent supply correct asset values.

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 precise imperative: 'Remove background from image.' This names a specific operation and resource, and the tool name plus sibling list make its niche — background removal rather than generic image editing, colorizing, upscaling, or generation — 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?

It does not explicitly compare against sibling create-image tools, but it provides clear operational context: the job is async, costs 5 credits, returns an id immediately, and should be followed by wait_for_image_project or polling. It also gives concrete file-path guidance, preferring Magic Hour file paths or presigned-upload results over hotlinks. There are no exclusions or alternative-tool callouts, so it is not a full 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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