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Aseprite MCP Tools

by midarikawa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_convolution_matrices

Retrieve built-in convolution matrix names to use with apply_convolution for filtering images or sprites in Aseprite.

Instructions

List the built-in convolution-matrix names usable with apply_convolution.

Returns: JSON array of matrix resource names.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_convolution_matrices"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, but the description clearly states the tool is a listing operation returning a JSON array of names. No hidden behaviors or side effects are indicated, which is appropriate for a simple read-only tool.

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 two sentences: one for purpose and one for return value. No extraneous information, highly efficient.

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?

The tool has no parameters, no output schema, and is a simple list. The description provides the return type (JSON array) and the content (matrix resource names). It is sufficient for this minimal tool, though additional detail about the format of names could be included.

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?

The input schema has zero parameters, so no parameter documentation is needed. The description does not add any parameter information, which is acceptable as baseline.

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 lists 'built-in convolution-matrix names' and explicitly links them to 'apply_convolution'. This distinguishes it from sibling tools by specifying the exact resource type and its usage context.

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 implies usage before apply_convolution by stating the matrices are 'usable with' it. However, no explicit when-to-use or when-not-to-use guidance is provided, nor are alternatives mentioned.

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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