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post-patch-inferences

Apply image patches to modify assets by merging or erasing content using specified positions and modes within the Scenario.com MCP Server.

Instructions

Patch an asset with an image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
patchNo
imageYesThe input image as a data URL (example: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVQYV2NgYAAAAAMAAWgmWQ0AAAAASUVORK5CYII=") or the asset ID (example: "asset_GTrL3mq4SXWyMxkOHRxlpw")
backgroundColorNoThe background color as an hexadecimal code (ex: "#FFFFFF"), an html color (ex: "red") or "transparent" if "format" is "png". Default to "white"
formatNoThe output format. Default to "png"
positionNo
allowOverflowNoWhether to allow the merged image to extend the size of the original (when x or y are negative or merged image is bigger)
cropNo
Behavior1/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the action ('patch an asset with an image') without mentioning effects (e.g., whether it modifies the original asset, creates a new one, requires permissions, has rate limits, or returns a result). For a tool with 9 parameters and no output schema, this is a critical omission.

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 a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for its content, though it's under-specified rather than concise. Given the scoring criteria, it earns full points for brevity and structure.

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

Completeness1/5

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

Given high complexity (9 parameters, nested objects, no annotations, no output schema), the description is severely incomplete. It doesn't explain what 'patch' entails, the tool's behavior, output format, or parameter roles. For a mutation tool with extensive input schema, this minimal description is inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 56%, so the description should compensate for gaps. It mentions 'an image' but doesn't explain parameters like 'originalAssets', 'dryRun', 'patch', 'backgroundColor', 'format', 'position', 'allowOverflow', or 'crop'. The description adds minimal value beyond the schema, failing to clarify the purpose or interactions of these parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Patch an asset with an image' states a verb ('patch') and resource ('asset'), but it's vague about what 'patch' means in this context (e.g., merging, overlaying, modifying). It doesn't distinguish from siblings like 'post-img2img-inferences' or 'post-inpaint-inferences', which might involve similar image operations. The purpose is minimally stated but lacks specificity.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'post-img2img-inferences', 'post-inpaint-inferences'), the description offers no context, prerequisites, or exclusions. This leaves the agent without direction on appropriate usage scenarios.

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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