How do I use scuffed-painter?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@scuffed-painter Draw a green triangle with a gold circle and a black line on a 200x200 canvas." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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scuffed-painter

by viktor-haag

Overview Schema Related Servers Score Discussions

Python

Hybrid

Scuffed Painter

An MCP (Model Context Protocol) server that renders drawing commands into raster images using Pillow. It exposes two tools:

draw_image — render the commands and return the result as an inline PNG image.
draw_image_to_file — render the commands and save the result to a file.

Five shapes are supported, each a JSON object discriminated by its "shape" field:

shape	parameters
`line`	`start`, `end`, `color`, `width`
`rect`	`box` (left, top, right, bottom), `fill`, `outline`, `width`
`circle`	`center`, `radius`, `fill`, `outline`, `width`
`ellipse`	`box` (x0, y0, x1, y1), `fill`, `outline`, `width`
`triangle`	`points` (exactly three vertices), `fill`, `outline`, `width`

Commands run in list order on a fixed-size white canvas; later commands overpaint earlier ones. Colors are CSS color names or hex codes.

Installation

pip install .

This provides the scuffed-painter console script, which launches the MCP server over stdio (the default). python -m scuffed_painter is an equivalent alternative. The same command can also serve the server over HTTP (MCP Streamable HTTP transport):

scuffed-painter                              # stdio transport (default)
scuffed-painter --transport http             # HTTP on 127.0.0.1:8000
scuffed-painter --transport http --host H --port P

--host and --port only apply to the HTTP transport; the defaults are 127.0.0.1 and 8000. You may enable CORS with --enable-cors if necessary.

Related MCP server: openai-imagegen-mcp

MCP host configuration

stdio

Add the server to your MCP host's stdio server configuration:

{
  "mcpServers": {
    "scuffed-painter": {
      "command": "scuffed-painter"
    }
  }
}

Note: on a host with a restricted PATH or when running inside a virtualenv, use the absolute path to the installed scuffed-painter script instead (e.g. the one reported by which scuffed-painter).

Streamable HTTP

Start the server yourself, then point your MCP host at the endpoint (the path is /mcp):

scuffed-painter --transport http --host 127.0.0.1 --port 8000

{
  "mcpServers": {
    "scuffed-painter": {
      "type": "streamable-http",
      "url": "http://127.0.0.1:8000/mcp"
    }
  }
}

(Adjust the key names to match your MCP host's configuration format.)

Note: when draw_image_to_file is used, output_path refers to the machine the server runs on. Over stdio that is the same host as the MCP client; when the server is reached over HTTP, it is the server's host instead.

Example tool call

{
  "width": 200,
  "height": 200,
  "commands": [
    {"shape": "circle", "center": [100, 80], "radius": 50, "fill": "gold", "outline": "orange", "width": 3},
    {"shape": "triangle", "points": [[50, 170], [150, 170], [100, 70]], "fill": "darkgreen"},
    {"shape": "line", "start": [20, 185], "end": [180, 185], "color": "black", "width": 2}
  ]
}

Development

Repository layout:

scuffed_painter/          the package
  server.py             server, tools, command models, rendering
  __init__.py           re-exports mcp, defines __version__
  __main__.py           python -m scuffed_painter support
  py.typed              typing marker
tests/                  pytest suite (in-process fastmcp.Client)

Setup and running the tests:

pip install -e ".[dev]"
python -m pytest

Available Tools

2 tools

draw_imageA

Draws a picture with pillow and returns the resulting image as inline PNG image content. This is especially usefull for (chat) applications that can directly display images.

width: canvas width in pixels (positive integer). height: canvas height in pixels (positive integer). commands: list of drawing commands, executed in order; each command is a JSON object with a "shape" field selecting one of: line, rect, circle, ellipse, triangle.

Supported shapes and their fields: line: "shape": "line", fields: start (x, y), end (x, y), color (default black), width (default 1) rect: "shape": "rect", fields: box (left, top, right, bottom), fill (default black, None for none), outline (default None), width (default 1) circle: "shape": "circle", fields: center (x, y), radius, fill (default black, None for none), outline (default None), width (default 1) ellipse: "shape": "ellipse", fields: box (x0, y0, x1, y1), fill (default black, None for none), outline (default None), width (default 1) triangle: "shape": "triangle", fields: points (three (x, y) vertices), fill (default black, None for none), outline (default None), width (default 1)

Commands are validated individually. Invalid commands are skipped, and a warning listing them (1-based positions) is included in the result; the image still contains all valid commands in order. If no command is valid, the call fails with a listing of the per-command errors.

Coordinate system: The coordinate system has its origin at the top-left corner; x increases to the right, y increases downward. All coordinates are in pixels on a fixed-size white background canvas; drawing outside the canvas bounds is clipped (harmless). Commands run in list order, and later commands overpaint earlier ones.

Color format: Color as a CSS color name (e.g. 'red', 'darkorange') or hex code (e.g. '#8b4513'). Values are passed through to Pillow.

Paint semantics: fill=None gives an unfilled shape; outline draws the border; width is the stroke width in pixels for line/rect/circle/ellipse/triangle.

Example (200x200 canvas): { "width": 200, "height": 200, "commands": [ {"shape": "circle", "center": [100, 80], "radius": 50, "fill": "gold", "outline": "orange", "width": 3}, {"shape": "triangle", "points": [[50, 170], [150, 170], [100, 70]], "fill": "darkgreen"}, {"shape": "line", "start": [20, 185], "end": [180, 185], "color": "black", "width": 2} ] }

ParametersJSON Schema

Name	Required	Description	Default
`width`	Yes
`height`	Yes
`commands`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden, and it delivers thoroughly. It discloses invalid-command skipping with warnings, total-invalid-command failure behavior, coordinate origin and direction, clipping, overpainting order, color formats, and pass-through to Pillow. This is far beyond what annotations alone would provide.

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?

Although the description is long, the complexity of the tool justifies the length. It is well-structured with clear sections for shapes, coordinate system, colors, paint semantics, and an example, and the core purpose is front-loaded in the first sentence.

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?

Given no annotations, no output schema, and a highly complex nested command format, the description covers everything an agent needs: complete shape schemas, defaults, validation behavior, failure modes, coordinate system details, and a full example. Nothing essential is missing.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by explaining width, height, and commands in depth. It documents every supported shape, all their fields, defaults, color formats, coordinate semantics, and provides a working example, so no parameter meaning is left to inference.

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 opens with a specific verb and resource: 'Draws a picture with pillow and returns the resulting image as inline PNG image content.' This clearly differentiates the tool from its sibling draw_image_to_file by emphasizing inline PNG output rather than file output, so an agent can select it correctly.

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 gives clear context for when to use this tool: 'especially useful for (chat) applications that can directly display images.' It does not explicitly name draw_image_to_file as the alternative for file output, but the inline-image framing strongly implies that distinction, leaving little ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

draw_image_to_fileA

Draws a picture with pillow and saves the resulting image to a file.

output_path: absolute file path to save to; the image format is inferred from the extension (e.g. .png, .jpg). Parent directories are NOT created. width: canvas width in pixels (positive integer). height: canvas height in pixels (positive integer). commands: list of drawing commands, executed in order; each command is a JSON object with a "shape" field selecting one of: line, rect, circle, ellipse, triangle.

Color format: Color as a CSS color name (e.g. 'red', 'darkorange') or hex code (e.g. '#8b4513'). Values are passed through to Pillow.

Paint semantics: fill=None gives an unfilled shape; outline draws the border; width is the stroke width in pixels for line/rect/circle/ellipse/triangle.

Example (200x200 canvas): { "output_path": "/home/myuser/documents/picture.png", "width": 200, "height": 200, "commands": [ {"shape": "circle", "center": [100, 80], "radius": 50, "fill": "gold", "outline": "orange", "width": 3}, {"shape": "triangle", "points": [[50, 170], [150, 170], [100, 70]], "fill": "darkgreen"}, {"shape": "line", "start": [20, 185], "end": [180, 185], "color": "black", "width": 2} ] }

ParametersJSON Schema

Name	Required	Description	Default
`width`	Yes
`height`	Yes
`commands`	Yes
`output_path`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses that parent directories are not created, invalid commands are skipped with warnings, no-valid-command fails, later commands overpaint earlier ones, drawing outside the canvas is clipped, and colors follow specific formats.

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 long, but the length is justified by the tool's complexity and the absence of schema-level documentation. It is front-loaded with a one-sentence purpose, then organized into clear labeled sections with a concrete example.

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 four required parameters, a free-form commands array, and no annotations, the description covers nearly every decision an agent needs to make: file path rules, canvas size, command shapes, validation behavior, coordinate system, painting order, and colors. The presence of an output schema means return-value details are not required.

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

Parameters5/5

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

The input schema provides only names and basic types, so schema coverage is effectively 0%. The description compensates completely by explaining output_path semantics, width/height meaning, command structure, all supported shape fields, defaults, and coordinate/color conventions.

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 opens with a specific verb-resource pair: it draws a picture and saves it to a file. It also distinguishes itself from the sibling draw_image by emphasizing file output, so an agent can tell the tools apart.

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

Usage Guidelines3/5

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

The saving-to-file behavior implies when this tool is appropriate, but the description never explicitly states when to use it versus draw_image or what conditions would favor one over the other. This is usable but implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

2 tool updatesv0.1.0Aug 27, 2026
- First observeddraw_image
- First observeddraw_image_to_file

Install Server

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TDQS

A4.6/5.0

Disambiguation5/5

The two tools split cleanly by output destination: draw_image_to_file persists to disk, while draw_image returns inline PNG content. Their names and the presence of output_path only in the file variant make misselection unlikely.

Naming Consistency5/5

Both tools follow the same draw_image verb-object prefix, with the file-save variant adding the clear _to_file suffix. The naming is snake_case and consistent across the whole set.

Tool Count4/5

Two tools is slightly below the typical 3-15 range, but the count is reasonable for a narrow painter: one output mode is file saving and the other is inline PNG. It feels minimal rather than excessive.

Completeness4/5

The core draw-and-output workflow is fully covered: all five shapes, validation, coordinate handling, and both file and inline delivery are present. Minor gaps remain, such as only having a fixed white background and no text or image-import tools, though these are workaround-able or outside the narrow scope.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

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Scuffed Painter

Installation

MCP host configuration

stdio

Streamable HTTP

Example tool call

Development

Available Tools

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

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