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SeedreamMCP

SeedreamMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Decompose an image with Seedream

seedream_decompose_image
Destructive

Decompose an image into a base image and up to 16 editable transparent layers for editing, compositing, or asset extraction.

Instructions

Decompose one image into a base image and up to 16 editable transparent layers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image": "<image>"
    },
    "name": "seedream_decompose_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
sizeNoOutput size: auto, 1K, 1.5K, or 2K.auto
imageYesOne PNG or JPEG URL/base64 image to decompose.
promptNoOptional elements to decompose; omit for automatic decomposition. Supports <bbox> coordinates.
watermarkNoWhether to add an AI-generated watermark.
callback_urlNoOptional webhook URL for async delivery.
output_formatNoBase image format; layers are always PNG.jpeg

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.15

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare the mutation profile (readOnlyHint=false, destructiveHint=true, non-idempotent, openWorld), so safety is covered. The description adds the layer-count ceiling ('up to 16'), but omits that this is an async job (callback_url param, get_task/get_tasks_batch siblings) and says nothing about cost, latency, or whether the source image is retained.

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?

A single tight sentence that front-loads the verb and resource and still conveys the output cardinality. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be re-explained, and the schema itself covers all parameters. However, for a destructive, open-world, non-idempotent async generation tool, the description omits how results are delivered and whether the operation is reversible, leaving a real gap for an agent deciding whether to call it.

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 all six parameters are already documented in the schema. The description adds the decomposition result structure but no parameter syntax or default behavior, so the baseline 3 applies.

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?

States a specific verb ('Decompose') plus the resource ('one image') and goes further by describing the output shape: a base image and up to 16 editable transparent layers. That distinguishes it from seedream_generate_image and seedream_edit_image on capability, though it never names those siblings explicitly.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no statement of when to prefer decompose over edit or generate, and no prerequisites (supported input types, async expectations). The reader must infer usage from the verb alone.

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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