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Inpaint Image

inpaint_image

Replace masked areas of an image with new content generated from a text prompt. Provide a source image and a grayscale mask where white indicates the region to fill.

Instructions

Inpaint (fill in) a masked region of an image based on a text prompt. Requires the original image and a grayscale mask (white = inpaint, black = preserve).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of output images (1-6).
maskYesGrayscale PNG mask as a URL or base64-encoded string. White = areas to inpaint, black = areas to preserve.
imageYesSource image as a URL or base64-encoded string.
modelNoModel to use.
styleNoStyle category.
promptYesDescription of what to generate in the masked area.
style_idNoCustom style ID.
substyleNoSubstyle.
random_seedNoSeed for reproducibility.
image_formatNoOutput format.
negative_promptNoWhat to avoid.
Behavior4/5

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

The description discloses that the tool modifies the image (not read-only) and details the mask semantics. It does not mention potential rate limits, size constraints, or that the original image may be transformed, but annotations already indicate it is a mutation. No contradictions.

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 sentences, no filler, front-loaded with the operation and key inputs. Every sentence adds value.

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?

Given 11 optional params and no output schema, the description covers the core functionality well. It could mention optional controls (style, seed) but they are documented in the schema. Overall adequate for an agent to understand the main purpose.

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 the schema already documents each parameter. The description adds minor emphasis on the mask's color meaning, which is already in the schema. No additional value beyond that.

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 states the operation ('inpaint'), the required inputs (image, mask, prompt), and the mask color semantics. It distinguishes from sibling tools like erase_region (which removes without filling) and replace_background (which targets the background).

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 description explains when to use (inpainting a masked region with a prompt) and the required inputs, but it does not explicitly state when not to use or mention alternative tools for similar tasks (e.g., erase_region for removal without filling).

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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