switch

Separate an image into layers

split_image_into_layers

Separate one of your own Switch images into editable layers with Seedream 5.0 Pro: a base plus up to 16 transparent PNG layers, each named and placed. Say which image (asset_id, or "my last image"), optionally what to separate, and the size. Charged only for the layers that actually come back; a failed split is fully refunded.

Input Schema

TableJSON Schema

Name	Required	Description
`size`	No	Output size for the layers. Auto follows the original image.
`asset_id`	Yes	The id of your image to separate. "my last image" also works.
`instruction`	No	Optional: what to separate, e.g. "the outfit and the bunny". Leave empty to separate everything the model finds.

TDQS

A4.4/5.0

Behavior5/5

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

The description adds substantial behavioral detail beyond the minimal annotation (readOnlyHint=false). It discloses the model version, output format (named/placed transparent PNGs), layer count limit (16), and crucially the billing/refund policy (charged only for layers returned, failed splits refunded). This gives the agent a clear expectation of side effects and costs.

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 two sentences with no redundancy. The first sentence defines the operation and output characteristics; the second gives usage syntax and the billing caveat. Every clause earns its place.

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?

The description conveys the essential behavioral contract: input requirements, output composition, and financial risk. It does not explain the delivery mechanism or how the resulting layer set can be accessed via sibling tools, but these are not necessary for using the tool correctly.

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?

The input schema already covers all three parameters with descriptions (100% coverage), including the 'my last image' alias for asset_id and the meaning of instruction and size. The description restates these but does not add new semantic information beyond the schema.

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 clearly and specifically states the tool's function: separating one of the user's own Switch images into editable layers using Seedream 5.0 Pro, producing a base plus up to 16 named and placed transparent PNG layers. It distinguishes itself from sibling layer management tools (export_layer_set, flatten_layer_set) by focusing on the split operation.

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 description provides clear invocation context: the user must specify an image (asset_id or 'my last image'), optionally an instruction for what to separate, and a size. It also implies an ownership constraint ('your own'). However, it does not explicitly contrast with alternative tools or state when not to use this tool.

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

A3.5/5.0

Disambiguation2/5

Several tools occupy nearly identical semantic ground: apply_iphone_realism and apply_ugc both describe casual phone-shot looks, upload_media and upload_reference_asset both accept uploads, and analyze_video overlaps heavily with analyze_video_report. The many apply_* style tools are essentially one tool parameterized by style, so agents can easily select the wrong one.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern such as generate_image, list_my_videos, get_editor_run, and upscale_video. A few outliers like voice, talking_avatar_video, and video_to_prompt do not use the same verb-first convention, but they are still readable and do not create significant confusion.

Tool Count1/5

At 55 tools, the surface is far beyond what is appropriate for an MCP server; many of these be collapsed or parameterized, especially the 10 apply_* style wrappers and several overlapping upload/status helpers. Even for a broad media platform, this scale forces a huge context window and makes selecting the right tool impractical.

Completeness2/5

The surface covers generation, media display, video analysis, and Editor workflows well, but there are obvious gaps in library lifecycle management: move_asset and create_folder are referenced in tool descriptions without being exposed, and there is no clean way to delete or reorganize media assets. Agents following the descriptions will try to call tools that do not exist.

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