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generate_app_icon

Create custom app icons by specifying text, colors, and size, and save them as PNG files.

Instructions

Generate an app icon image with custom text, colors, and size

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoIcon size in pixels (default: 1024)
textYesText to display on the icon (e.g., 'K' for Kanban)
fontSizeNoFont size as a percentage of icon size (0.0-1.0, default: 0.7)
textColorNoText color in hex format (e.g., '#000000' for black, default: '#000000')#000000
fontFamilyNoFont family name (optional, uses system default if not specified)
outputPathYesFull path where the icon PNG file should be saved
backgroundColorNoBackground color in hex format (e.g., '#FFFFFF' for white, default: '#FFFFFF')#FFFFFF
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'generate' without revealing that the tool writes a file to outputPath, whether it overwrites existing files, or what the output/return behavior is. This is a significant transparency gap for a tool that performs a file-side effect.

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?

The description is a single concise sentence with no wasted words. It is front-loaded with the core purpose, though it could arguably add a bit more detail without becoming verbose.

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

Completeness2/5

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

Given 7 parameters, no output schema, and no annotations, the description is too minimal to be fully contextual. It omits behavioral details like file saving, overwrite behavior, or any guidance on output, leaving significant gaps for an agent to infer.

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%, providing full meaning for all 7 parameters. The description adds only a high-level mention of text, colors, and size, which overlaps with schema. Baseline 3 is appropriate since the schema already does the heavy lifting.

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?

The description clearly states the tool generates an app icon image with custom text, colors, and size, using a specific verb and resource. However, it does not explicitly distinguish itself from the sibling tool generate_ios_app_icon_set, so it lacks explicit sibling differentiation.

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?

The description provides no guidance on when to use this tool versus generate_ios_app_icon_set, nor does it mention any context, prerequisites, or exclusions. It simply states what the tool does without indicating appropriate use cases.

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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