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Render an animation to APNG

riv_render_apng

Render .riv animations to animated PNG (APNG) with 24-bit color and transparent backgrounds, enabling alpha transparency for GitHub READMEs and web pages.

Instructions

Render a .riv animation (or state machine playback) to an animated PNG (APNG). Unlike GIF this supports 24-bit color plus full alpha transparency, and GitHub READMEs animate it like a regular image. Frames are rendered with a transparent background by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fpsNoFrames per second (default 20)
outNoOutput path, .apng or .png (default: <name>.apng alongside the .riv)
pathYesPath to the .riv file
loopsNoLoop count (0 = infinite, default 0)
widthNoOutput width (default 480)
heightNo
artboardNo
durationNoSeconds to render (default 2)
animationNoAnimation name (default: first state machine or animation)
backgroundNoCSS background color when transparent=false (default #ffffff)
transparentNoRender on a transparent background to keep APNG alpha (default true). Set false to composite onto 'background'
stateMachineNo
Behavior3/5

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

No annotations are present, so the description carries the disclosure burden. It adds useful behavioral detail such as transparent background by default and full alpha support. However, it does not mention side effects like file creation, output path defaults, or overwrite behavior, leaving some important gaps.

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?

Three focused sentences: the first states the function, the second gives format differentiation and a concrete use case, and the third notes the default transparency behavior. Every sentence earns its place with no redundancy.

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?

This is a 12-parameter render tool with no annotations and no output schema. The description covers basic purpose and a key differentiator, but omits important invocation details such as output path defaults, state machine vs animation selection, and interactions with transparent/background. It is adequate for tool selection but not for full correct invocation across all options.

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 75%, so the schema handles most parameter meanings. The description adds context around transparency defaults but does not explain undocumented params like height, artboard, or stateMachine. It provides some added value but is not heavily compensating.

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?

Clearly states it renders .riv animations or state machine playback to APNG, naming the exact resource and output format. It also distinguishes itself from GIF by highlighting 24-bit color and alpha transparency, which separates it from sibling tools like riv_render_gif.

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?

Provides a concrete use case comparison with GIF, noting APNG's support for full alpha transparency and GitHub README compatibility, which helps an agent decide when to choose this tool. It does not explicitly exclude alternatives like video or sprites, but the context is strong.

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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