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Split an image into layers

split_into_layers

Split a flat image (PNG, JPG or WebP) into named transparent layers, with the hidden parts behind objects filled in. Costs 1 credit per layer returned (at most 17); the account needs at least 5 credits to start. Pass the image as a public https URL or as base64. Waits up to 50 seconds; if it is still processing, call get_layers later.

Input Schema

NameRequiredDescriptionDefault
promptNoOptional: what to separate, in plain words, e.g. "the cat, the sofa and the background". Leave empty to let the model decide.
image_urlNoPublic https URL of the image.
image_base64NoThe image as base64, with or without a data: prefix. Use when there is no URL.
wait_secondsNoHow long to wait for the result before returning, 0 to 50 seconds.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=false, destructiveHint=false and openWorldHint=true. The description goes well beyond them by disclosing the pricing model per returned layer, the 17-layer ceiling, the 5-credit account prerequisite, the 50-second blocking window, and the fallback to get_layers — the exact facts an agent needs before committing credits.

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?

Three tight sentences with the core action front-loaded and cost, credit prerequisite and wait/fallback following in priority order. A small amount of content (input formats) duplicates the schema rather than earning new ground.

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?

With no output schema, the description carries the burden of describing results, and it does so by naming the return (named transparent layers) and bounding them (at most 17, one credit each). It omits failure semantics — whether credits are still charged on a failed or empty split, and what insufficient credits returns.

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 100%, so prompt, image_url, image_base64 and wait_seconds are already fully documented in the schema. The description restates the two input modes (public https URL or base64) but adds no syntax or behavioral nuance beyond what the schema already says; baseline 3 applies.

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?

States a specific verb and resource: split a flat image into named transparent layers, with the added detail that hidden parts behind objects get filled in. Formats accepted (PNG, JPG, WebP) are named, and the async sibling get_layers is referenced, so it is separable from its siblings without opening a schema.

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?

Gives concrete operating conditions: costs 1 credit per layer (max 17), account needs at least 5 credits, image supplied as public https URL or base64, and polling via get_layers if the 50s wait expires. It does not explain when to prefer unlock_layers or get_credits over this call, so the sibling routing is incomplete.

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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