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OpenSCAD logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 7:57:05 PM

OpenSCAD

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository3 GitHub StarsTotal stargazers on GitHub for the source repository (3 stars).Visit Website

An MCP server for rendering STL and PNG from OpenSCAD code.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "openscad": {
      "command": "npx",
      "args": [
        "-y",
        "skills"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

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Documentation Overview

OpenSCAD MCP Server

npm version npm downloads CI

An MCP (Model Context Protocol) server that renders PNG previews and STL geometry from OpenSCAD (SCAD) source code. It is designed to support iterative, agent-driven CAD workflows, where models can be previewed visually and exported for downstream use (e.g. fabrication, simulation, or inspection).

⚠️ Beta
This MCP server is currently in beta. Performance, APIs, and capabilities may change. Issues and contributions are welcome.

Use cases

  • Iterative agent-driven modeling
    Agents generate or modify OpenSCAD source, render PNG previews to evaluate shape and proportions, and refine the model across multiple turns.

  • Geometry artifact generation within MCP contexts
    Agents export STL files as concrete geometry artifacts that can be passed to downstream tools, stored, inspected, or handed off to other MCP-enabled systems.

  • Visual grounding for parametric design
    PNG previews provide visual grounding for parametric or programmatic SCAD code, reducing hallucination and enabling agents to reason about spatial changes.

  • Design validation and comparison
    Agents can render multiple variants of a model (e.g. parameter sweeps) and visually compare results before deciding which geometry to persist or export.

Available Tools

  • render_scad_png: Renders a PNG preview image from SCAD source.
    • Input: scadCode (string), optional width/height (numbers), optional cameraPreset and optional cameraPosition
      • cameraPreset: one of isometric, front, back, left, right, top, bottom
      • cameraPosition: { x, y, z }
    • Output: MCP ImageContent
  • export_scad_stl: Exports an STL generated from SCAD source.
    • Input: scadCode (string), optional filename (string)
    • Output: MCP embedded resource (STL)

Skill

This repository also includes an OpenSCAD iterative modeling skill that demonstrates how to use this MCP server to support an iterative SCAD β†’ PNG β†’ critique β†’ refine loop.

Limitations

  • Performance: Rendering complex SCAD models can be slow, especially in a WASM environment.
  • Feature support: Not all OpenSCAD features may be fully supported or may have limitations in the WASM version.
  • Fonts: Text rendering is not currently supported. Support is planned for a future release.

Installation

The published package is intended to run over stdio. Configure it in your MCP client using npx:

config.json
{
  "mcpServers": {
    "openscad": {
      "command": "npx",
      "args": ["-y", "openscad-mcp-server"]
    }
  }
}

Using the Skill

Agents skills are a simple, open format for giving agents new capabilities and expertise.

The most straightforward to use the OpenSCAD iterative modeling skill is to install it using the skills CLI:

Terminal
npx skills add fboldo/openscad-mcp-server --skill openscad-iterative-modeling

Local development

  • Install deps: bun install
  • Stdio (matches how clients run it): bun index.ts --stdio
  • HTTP (useful for manual testing): bun index.ts
    • Port: MCP_PORT (default 3000)
    • Endpoints: GET /health, MCP at POST /mcp
  • MCP Inspector: bun run dev

Similar Projects

  • jhacksman/OpenSCAD-MCP-Server This project provides a different approach relying on generating images from user prompts, followed by 3D reconstruction and even 3D printer discovery. It's a very interesting project, and I recommend checking it out if you are interested in OpenSCAD and MCP servers.

  • petrijr/openscad-mcp Similar to this project, but it uses a Python-based server and relies on the OpenSCAD CLI for rendering.

Relevant Links

  • OpenSCAD
  • OpenSCAD WASM
  • Model Context Protocol (MCP)
  • Bun

License

MIT β€” see LICENSE.

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about OpenSCAD

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "openscad": { "command": "npx", "args": ["-y", "OpenSCAD"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars3
GitHub Star CountTotal stargazers on GitHub representing community popularity (3 stars).
36Quality signal: Fair Β· 36/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools15/30
Adoption & activity2/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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