Aseprite MCP Tools

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get_color_stats

Identify palette issues by analyzing a frame's color usage, counting unique colors, and listing the most-used colors with their frequencies.

Instructions

Report the colors used in a frame and how often each appears.

Flattens the sprite (non-destructively) and histograms the frame. Use to check palette discipline: too many near-duplicate colors is a common pixel-art mistake.

Args: filename: Aseprite file to read frame_index: Frame to analyze, starting at 1 (default 1) top: How many of the most-used colors to list (default 16)

Returns: JSON with unique color count, opaque pixel count, and the top colors with usage counts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "filename": "<filename>"
    },
    "name": "get_color_stats"
  }
}

Input Schema

Name	Required	Description	Default
`top`	No
`filename`	Yes
`frame_index`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that it 'flattens the sprite (non-destructively)' and histograms the frame, providing key behavioral insight. However, it does not address permissions or performance.

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 reasonably concise with a clear structure: brief purpose sentence, use case hint, parameter list, and return info. It could be slightly tighter but is effective.

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

Completeness5/5

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

For a tool with no output schema, the description adequately covers the return format ('JSON with unique color count, opaque pixel count, and the top colors with usage counts'). All parameters are documented, and the output is described, making it complete.

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

Parameters4/5

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

Despite 0% schema description coverage, the description includes a full 'Args' section explaining each parameter's meaning (filename, frame_index, top). This adds value beyond the raw schema.

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's purpose: 'Report the colors used in a frame and how often each appears.' It also provides a specific use case ('check palette discipline'), making it distinct from related siblings like get_pixel_color or extract_palette.

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 suggests when to use the tool ('Use to check palette discipline'), but it does not explicitly mention when to avoid it or name alternatives from the sibling list.

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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