AI-driven 3D CAD via build123d: execute, render, measure, and export geometry interactively.
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Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"build123d-mcp": {
"args": [
"tool",
"run",
"--python",
"3.12",
"build123d-mcp@latest"
],
"command": "uv"
}
}
}Are you the author?
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An MCP (Model Context Protocol) server that exposes build123d CAD operations as tools, enabling AI assistants to build, inspect, and iterate on 3D geometry interactively.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@anthropic-ai/sandbox-runtime' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
Anthropic Sandbox Runtime Incorrectly Implemented Network Sandboxing
Due to a bug in sandboxing logic, `sandbox-runtime` did not properly enforce a network sandbox if the sandbox policy did not configure any allowed domains. This could allow sandboxed code to make network requests outside of the sandbox. A patch for this was released in v0.0.16. Thank you to https://github.com/bendrucker for reporting this issue!
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Give your AI CAD eyes.
build123d-mcp is not a standalone chatbot or CAD program. It is a CAD toolbox that an AI/LLM app can use through MCP.
With an LLM app such as Claude, Cursor, VS Code, Continue, Cline, or Codex CLI, build123d-mcp lets the assistant create build123d CAD models, render previews, measure geometry, fix mistakes, and export files such as STEP, STL, SVG, and DXF. Instead of writing a whole CAD script blindly, the assistant can build a part in small steps and check the result as it goes.
On the public CADGenBench leaderboard in June 2026, using build123d-mcp raised the same model's score from 0.360 to 0.457 and CAD validity from 88% to 100%.
Most users should start with the local MCP server setup:
build123d-mcp as a local subprocess.Use GitHub Codespaces instead if you want a browser-only trial or a ready-made development workspace with Copilot Chat, Python, uv, and the CAD dependencies already installed.
Use HTTP mode only for advanced deployments where you are hosting the MCP server yourself.
You need:
No repository clone is needed for normal use. First check that the package can start:
uv tool run --python 3.12 build123d-mcp@latest --version
Then add the same command to your AI app's MCP config. The common pieces are:
command: uv
args: ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
Python 3.11, 3.12, 3.13, and 3.14 are supported. The examples use 3.12 because it is a conservative default, and uv can download it if you do not already have it installed.
The pieces are:
The server normally runs over stdio. Your AI app starts it as a local subprocess when it needs the CAD tools.
Add this to your project's .mcp.json, or to ~/.claude/mcp.json for global
use:
{
"mcpServers": {
"build123d-mcp": {
"command": "uv",
"args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
}
}
}
Restart Claude Code after editing.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json on macOS,
or %APPDATA%\Claude\claude_desktop_config.json on Windows:
{
"mcpServers": {
"build123d-mcp": {
"command": "uv",
"args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
}
}
}
Restart Claude Desktop after saving.
Open Settings -> MCP and add a new server entry, or edit
~/.cursor/mcp.json:
{
"mcpServers": {
"build123d-mcp": {
"command": "uv",
"args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
}
}
}
Use the same command and arguments in whichever MCP config your AI app or extension reads. The exact file