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by io.github.magichourhq

head_swap_create_image

Swap a head onto a body image. Each image costs 10 credits. Output resolution depends on your subscription; you may set max_resolution lower than your plan maximum if desired.

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
NameRequiredDescriptionDefault
nameNoGive your image a custom name for easy identification.Head Swap - dateTime
assetsYesProvide the body and head images for head swap
max_resolutionNoConstrains the larger dimension (height or width) of the output. Omit to use the maximum allowed for your plan (capped at 2048px). Values above your plan maximum are clamped down to your plan's maximum.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUnique 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_chargedYesThe 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.

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
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 it does so excellently. It discloses the 10-credit cost, subscription-dependent output resolution, the async job lifecycle, the need to poll or use a wait helper, and the risk that hotlinked media URLs may fail — all important behavioral traits beyond a simple 'creates an image' statement.

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 longer than average but well-structured with a front-loaded purpose sentence and clearly separated MCP guidance bullets. The additional length is justified by the genuinely useful async and file-handling details, though a small amount of repetition with the schema could have been trimmed.

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 and the lack of annotations, the description covers nearly everything an agent needs: cost, resolution constraints, async submission, how to retrieve results, status values, download URLs, and safe file input practices. The output schema covers return values, so the description does not need to restate them.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful value by explaining the file-path preference (`file_path` from upload flow over hotlinked URLs) and clarifying how `max_resolution` interacts with plan limits. This goes beyond what the schema already states.

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 opening sentence, 'Swap a head onto a body image,' names a specific verb and resource, making the operation immediately understandable. It does not explicitly differentiate itself from siblings like body_swap_create_image or face_swap_photo_create_image, but the operation is semantically distinct enough to avoid major confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is strongly implied by the first sentence and reinforced by the details on credits, resolution, and async behavior. However, there is no explicit guidance on when to choose this tool over related siblings or when not to use it, so this dimension remains at the implied-usage level.

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

A3.6/5.0
Disambiguation3/5

Most generation tools target distinct media types or effects (e.g., clothes changer, head swap, lip sync), but several boundaries blur: ai_image_editor_create_image is a generic edit tool that overlaps conceptually with ai_face_editor_edit_image, ai_image_upscaler_create_image, and background remover. The wait_for_*_project helpers also overlap functionally with the *_projects_retrieve_details status tools, and ai_voice_cloner_create_audio vs. ai_voice_generator_create_audio are easy to confuse by name.

Naming Consistency2/5

Naming conventions are mixed: many tools follow ai_<product>_create_<media>, but others are product-first (animation_create_video, body_swap_create_image) and resource-group tools follow a different noun_verb pattern (audio_projects_retrieve_details, video_projects_delete). Verbs are inconsistent too (create_image, edit_image, detect_faces, retrieve_details, wait_for, fetch), so an agent cannot reliably predict the next tool name.

Tool Count2/5

At 44 tools, the set is heavy: it includes 27 generation tools plus three wait helpers, three status retrieval tools, three delete tools, three fetch helpers, and upload/ping utilities. While the underlying product is broad, many helpers could be consolidated, and the overall surface exceeds the range where each tool earns a clear place.

Completeness3/5

The lifecycle is mostly covered for image, video, and audio projects: create, poll/retrieve, fetch download, delete, and file upload/presigned-URL generation are all present. However, there is no project listing or cancel operation, and face detection only has detect/details with no delete or wait helper, leaving some workflow gaps an agent must work around.

Resources