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asset_export_bundle

Export a 1024² master PNG into iOS AppIconSet, Android adaptive icons, PWA maskable icons, visionOS parallax, Flutter launcher, and favicon set. Runs locally via sharp, no API key required.

Instructions

Fan out a 1024² master PNG into the full platform bundle (iOS AppIconSet, Android adaptive, PWA maskable, visionOS parallax, Flutter launcher, favicon set). MCP equivalent of p2a export master.png. No API key required; runs entirely on sharp. Use when the LLM has a master (inline_svg saved, api-mode result, or user-supplied hand-authored PNG) and needs the platform fan-out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
master_pathYesAbsolute path to the 1024² master PNG. Resized up front to RGBA 1024².
platformsNoWhich platform bundles to emit. Defaults to all.
out_dirNoOutput directory. Defaults to ./assets/bundle-<stem>-<timestamp> so repeated runs don't clobber.
bgNoBackground color hex for iOS 1024 marketing (opaque), Android adaptive BG, favicon apple-touch. Defaults white.
app_nameNoShort name for the PWA manifest.
themeNotheme_color hex for the PWA manifest.
ios18NoWhen true, also emit iOS 18 dark + tinted 1024² appearance variants.
Behavior4/5

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

The description discloses key behavioral traits beyond annotations: no API key required, runs on sharp, resizes input to RGBA 1024², and uses a unique default output directory to avoid clobbering. This adds value as annotations only have openWorldHint: false.

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 three sentences long, all relevant. The first sentence states the core function, the second provides the MCP equivalent, and the third gives usage conditions. No wasted words.

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 the moderate complexity, 7 parameters with full schema coverage, and no output schema, the description adequately covers why, when, and basic behavior. It could be improved by briefly noting that output is a set of platform-specific bundles, but overall it is complete enough.

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 parameters are already well-documented. The description adds minimal additional meaning beyond the schema, such as the default for bg being white and out_dir being timestamped. 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 clearly states the tool fans out a 1024² master PNG into multiple platform bundles, listing specific platforms (iOS, Android, PWA, etc.). It provides the MCP equivalent command and distinguishes from siblings by specifying the comprehensive bundle generation, unlike single-icon tools like asset_generate_app_icon.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool: when the LLM has a master PNG from various sources and needs the platform fan-out. It does not explicitly mention when not to use or list alternatives, but the context is clear enough for an agent to decide.

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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