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recraft_generate_background

Fill masked background areas within an image's existing canvas. Use a white mask to specify the region to replace, and black to preserve original content, generating new background from your prompt.

Instructions

Fill masked background areas within an image's existing canvas (same size in, same size out). White mask = fill area, black = preserve. For expanding the canvas beyond the original image bounds, use recraft_outpaint instead. V3/V3 Vector only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of images to generate (1-6)
modelNoModel (V3 only)recraftv3
styleNoStyle name
promptYesDescription of background to generate
controlsNoGeneration controls
style_idNoCustom style UUID
file_pathYesLocal path to source image
mask_pathYesLocal path to mask (white=fill, black=preserve)
text_layoutNoText placement on image
negative_promptNoWhat to exclude
response_formatNoResponse formaturl

Schema Changelog

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

  1. First observedv1.2.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It reveals important non-obvious behavior: output stays within original bounds, white mask means fill, black means preserve, and only V3/V3 Vector is supported. It does not discuss output format or side effects, but the core behavioral traits are disclosed.

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?

Two dense, front-loaded sentences cover the core function, mask semantics, an explicit alternative, and model restriction without any filler. Every sentence 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?

For a complex tool with 11 parameters and no output schema, the description gives enough context to call it correctly: required inputs are clear via schema, and the key differentiator from recraft_outpaint is stated. It could more thoroughly distinguish itself from other background tools like recraft_replace_background or recraft_inpaint, but the essential invocation context is complete.

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 coverage is 100%, so the schema already documents all 11 parameters in detail. The description adds the same-size constraint and reinforces mask semantics, but it does not materially explain parameters beyond what the schema already provides. Baseline 3 is appropriate.

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 states a specific action (fill masked background areas), the exact resource (existing canvas), and a hard constraint (same size in, same size out). It also clarifies mask semantics, leaving no ambiguity about what the tool does.

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

Usage Guidelines5/5

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

It explicitly says when to use this tool (within existing canvas) and when not to (expanding canvas), naming the alternative recraft_outpaint directly. The V3/V3 Vector model restriction also provides clear eligibility guidance.

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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