build123d-mcp

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.

Why

When using an AI to write build123d scripts, the AI writes blind — it cannot see the geometry it produces. This server closes the feedback loop: the AI can create geometry, render views, query dimensions, and catch errors incrementally rather than writing complete scripts and hoping they are correct.

Tools

Core

• execute — run build123d Python code in a persistent session; use show(shape, name) to register named parts
• reset — clear session back to empty state (namespace, shapes, snapshots)

Geometry inspection

• measure — full geometric summary: volume, area, topology, bounding box, centre of mass, inertia tensor, face-type inventory
• clearance — minimum distance (mm) between two named shapes
• cross_sections — cross-sectional areas at evenly spaced planes along X/Y/Z; useful for detecting voids and wall-thickness variation
• resolve — evaluate a selector expression (e.g. .faces().filter_by(Axis.Z).last()) against a named object and return a geometry descriptor
• find_holes / find_bosses / find_hole_patterns — feature recognition: coaxial drill + counterbore + spotface stacks as one hole record (axis, location, diameter, depth, bottom: through/flat/drill_point/unknown), external bosses with height, bolt-circle and linear-array patterns
• analyze_printability — BREP-exact FDM printability analysis: overhangs, thin walls, minimum features, bed fit, tip-over risk
• session_state — full JSON snapshot of active shapes, named objects, snapshot names, and Python namespace variables
• last_error — details of the last failed execute(): type, message, line number, and code excerpt

Viewing

• render_view — render one or more shapes as PNG / SVG / DXF; auto-detects 3D vs 2D inputs (composed dimensioned drawings via build123d.drafting rasterise via ezdxf+matplotlib); supports assembly compositing, high-quality tessellation, cross-section clip planes, and optional labels for named shapes or specific faces/edges

Engineering drawings

• suggest_view_layout — auto-calculate safe page positions for a standard multi-view drawing layout
• view_axes — world-to-page axis mapping for a projected view, computed analytically before rendering
• render_drawing — rasterise an SVG file from disk to PNG
• inspect_drawing — structured bbox/annotation report for a 2D drawing (session objects or an SVG on disk)
• lint_drawing — structural drawing-quality checks: label/geometry divergence, overlapping annotations, page overshoot
• save_drawing_annotations — write a .dims.json sidecar capturing label metadata alongside an exported SVG

Import / export

• export — export as STEP / STL / DXF / SVG (or comma-separated like step,stl); auto-detects 2D vs 3D shape and routes to the appropriate format; targets a named object, the current shape, or * for all objects as an assembly
• import_cad_file — load a STEP or STL file as a named object for comparison

Comparison

• shape_compare — compare two named shapes by volume, bbox, topology, and centre offset
• align_check — check alignment between two named objects along an axis (flush / center / clearance modes)

Session checkpoints

• save_snapshot / restore_snapshot / diff_snapshot — checkpoint, recover, and compare geometric state

Part library (requires --library flag)

• search_library — search the part library by keyword; returns full parameter specs
• load_part — load a named part with optional parameter overrides

Utility

• version — return the server version
• health_check — verify VTK/SVG/STEP/STL dependencies work end-to-end
• repair_hints — get targeted fix suggestions for a given execute() error message
• workflow_hints — guidance on using the tools effectively
• script — assemble a reproducible Python script from the session's executed code blocks
• install_skill — copy a b123d workflow skill (modeling or drawing) into the current project

Resources

Read-only MCP resources available to LLM clients:

• build123d://quickref — build123d API quick reference (primitives, booleans, positioning, selectors, fillets)
• build123d://selectors — task-indexed selector cookbook (get the top face, find circular edges, filter by area/length/radius, Select.LAST in builder context, fillet detection)
• build123d://drafting — code-first 2D engineering drawings cookbook (project a 3D part, dimension with ExtensionLine/DimensionLine, tolerances, hole-table pattern, multi-view sheet, title block, export to DXF)
• build123d://drafting-api — API reference for build123d-drafting-helpers, generated from the installed library (exact signatures for Dimension, Leader, TitleBlock, Drawing, and every other public symbol)
• build123d://session — live session state as JSON (current shape, named objects, snapshots, variables)
• build123d://bd_warehouse — catalogue of pre-built parametric parts from bd_warehouse (bearings, fasteners, gears, pipes, threads, and more)

build123d version: examples in quickref and selectors are tested against build123d 0.10.x and 0.11.x (soft-pinned in pyproject.toml as >=0.10,<0.12). The exact installed version is reported at the top of each resource. If you need a different build123d version, override the dependency and verify the examples still match the API.

Prompts

• start-cad-session — primes a new CAD design session with the task description and step-by-step workflow reminders

See llms.md for full tool reference and usage patterns.

Recommended workflow

Build complexity falls into two tiers and the right approach differs between them.

Simple shapes (a few primitives, up to ~5 booleans): build entirely in execute().

Complex shapes (IsoThread, multi-body fillets, high face counts): the execute() timeout (default 120 s) is a hard ceiling. The efficient pattern is:

1. Probe in the MCP — small execute() calls to discover API signatures, size strings, and face counts. Use dir() and import inspect; inspect.signature(ClassName) freely.
2. Build in a Python script — run it with Bash (or your shell). No timeout, full Python.
3. Import and verify in the MCP:

   import_cad_file("/path/to/part.step", "part")
   measure("part")          # verify volume, topology, bounding box
   render_view(objects="part")  # visualise
   

Timeout note: the default is 120 s. Raise it with --exec-timeout N or BUILD123D_EXEC_TIMEOUT=N. When a timeout fires, all session state is lost (worker is restarted) — you must re-run any setup code.

Sandboxed-host note: if every execute() fails with "Worker process failed to start", your MCP host is likely blocking subprocess creation (seen with sandboxed hosts on Windows). Relaunch with --in-process or BUILD123D_IN_PROCESS=1 — a degraded mode that runs the CAD session inside the server process: no crash containment, no operation timeouts.

Import note: after import_cad_file() the shape is a named session object. Always render it by name (objects="part") when other shapes from the same build are also in session — two co-located shapes cause Z-fighting (striped colour artifacts). STL imports produce a shell (volume = 0); render_view and measure work, but clearance() and boolean operations require a solid.

bd_warehouse fasteners

bd_warehouse is a full fastener system, not just a thread library. Always:

1. Probe sizes first (correct string format is "M6-1" not "M6-1.0"):

   from bd_warehouse.fastener import CounterSunkScrew
   print(CounterSunkScrew.sizes("iso10642"))
   

2. Instantiate the fastener object, then pass it to the hole operation — never compute head geometry or tap-drill diameters manually:

   from bd_warehouse.fastener import CounterSunkScrew, CounterSinkHole, TapHole
   screw = CounterSunkScrew(size="M6-1", fastener_type="iso10642", length=10)
   
   with BuildPart() as wheel:
       Cylinder(radius=20, height=10)
       CounterSinkHole(fastener=screw, depth=10)   # countersunk through-hole
       TapHole(fastener=screw, depth=8)             # tapped bore
   

See build123d://bd_warehouse (MCP resource) for the full catalogue and usage patterns.

Security

Unlike CAD MCP servers that simply exec() user code, build123d-mcp ships with defence-in-depth sandboxing so the server is reasonable to expose to LLM-generated and untrusted prompts. Three layers, all applied before user code runs:

1. AST inspection — rejects imports of anything outside the allowlist (build123d, bd_warehouse, math, numpy, inspect, plus the rest of the safe stdlib subset and a curated set of geometric OCP submodules), blocks eval/exec/compile/open, and refuses dunder attribute access (the most common Python sandbox-escape route).
2. Restricted builtins — the __builtins__ exposed to user code has the dangerous functions removed and __import__ rewrapped to enforce the same allowlist at runtime, so a payload that bypasses the AST check still hits the wall on import.
3. Execution timeout — wall-clock limit (default 120 s, --exec-timeout N to override) enforced via SIGALRM, with the worker process restarted on breach so a hung script can't hold the session forever. In --in-process mode this layer is absent on Windows (no SIGALRM, no worker to restart) — a runaway script blocks the server.

Filesystem I/O modules (os, pathlib, shutil), networking (socket, urllib, requests), shell access (subprocess), and the OCP file-I/O submodules (STEPControl, IGESControl, OSD, …) are all blocked. Path traversal is rejected for export() and render_view(save_to=).

This is not a perfect sandbox — memory exhaustion isn't bounded, and Python introspection chains via build123d internals could in principle escape — but it raises the bar significantly against realistic prompt-injection payloads.

The part library is trusted input. Files under --library run with the same restricted builtins as user code, but the AST check inspects only each file's own top-level imports — it is a guard against accidents, not sandbox-equivalent isolation. Point --library only at directories you control, never at untrusted downloads.

Extending or relaxing the sandbox

Two CLI flags let you adjust the import policy without giving up the rest of the layers:

• --allow-imports scipy,pandas — extend the allowlist with named modules. Each entry permits the named root and all its submodules. Use for CAD scripts that need extra packages.
• --allow-all-imports — disable the import allowlist entirely. The other layers (restricted builtins for open/eval/etc, exec timeout, dunder-attribute block) still apply. Use only in trusted environments or under OS-level isolation (see below).
• --no-sandbox — disable all sandbox layers: the AST check is skipped and user code runs with unrestricted builtins (open/eval/exec/__import__). The exec timeout still applies. Dangerous — for trusted, isolated environments only (e.g. a benchmark harness); never expose to untrusted input. The import allowlist is lifted too (the AST check is skipped entirely), so --allow-all-imports is redundant alongside it.

These flags also accept their values via env var (BUILD123D_ALLOW_IMPORTS, BUILD123D_ALLOW_ALL_IMPORTS, BUILD123D_NO_SANDBOX).

Note: hasattr() and dir() are permitted by the default sandbox; getattr/vars/eval/exec/open and explicit dunder access are blocked. Use --no-sandbox if you need the blocked ones.

Stronger isolation: OS-level sandboxing

For deployments that need stronger guarantees than Python-level checks (e.g. exposing the server to truly untrusted input, or running with --allow-all-imports), wrap the whole MCP server in an OS-level sandbox:

• @anthropic-ai/sandbox-runtime — Anthropic's official sandbox runtime, designed exactly for this. The Claude Code docs explicitly call out wrapping MCP servers: npx @anthropic-ai/sandbox-runtime <command-to-sandbox>.
• Docker / containers — generic approach; many community MCP-sandbox wrappers exist (e.g. pottekkat/sandbox-mcp, Automata-Labs-team/code-sandbox-mcp). Run build123d-mcp inside a minimal container with no host filesystem mounts and no network egress.
• Claude Code's sandbox (/sandbox command, macOS Seatbelt or Linux bubblewrap) — if you're running build123d-mcp under Claude Code, the host's sandbox already restricts what subprocesses can touch.
• Cursor / IDE dev containers — Cursor doesn't ship MCP-specific sandboxing, but you can run the server inside a dev container that the IDE attaches to.

Inside any of these, --allow-all-imports becomes a reasonable default: the OS-level isolation handles the security, and the Python-level allowlist becomes redundant friction. The recommended high-security recipe is sandbox-runtime (or a container) + --allow-all-imports + a strict exec timeout.

Requirements

• uv
• An MCP-compatible client (Claude Code, Claude Desktop, Cursor, etc.)

All Python dependencies (build123d, vtk, etc.) are installed automatically by uv.

Installation

No clone needed. Install directly from PyPI:

pip install build123d-mcp

Or just use uv tool run — it fetches and runs the package in one step with no prior install required (see below).

Adding to MCP clients

The server runs over stdio — the client launches it as a subprocess using uv tool run build123d-mcp.

Note on Python version. The examples below pass --python 3.12, but Python 3.11, 3.12, 3.13, and 3.14 are all supported and CI-tested. 3.12 is just a safe, widely-available default — swap in whichever interpreter you have. uv will auto-download a managed Python if you don't already have one.

Note on @latest. The examples request build123d-mcp@latest so each launch re-resolves to the latest published release instead of reusing uv's cached environment — without it, the client can stay pinned to whatever version uv first cached and silently miss releases. The trade-off is a short dependency-resolution step at every startup (and it needs network access to check for updates). Use plain build123d-mcp if you prefer faster, offline-capable starts and update manually with uv tool upgrade build123d-mcp. (Older versions of this README passed --upgrade instead; recent uv ignores that flag in uv tool run and warns on every launch — swap to @latest if you have the old config.)

Claude Code

Add to your project's .mcp.json (or ~/.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. The tools appear automatically once connected.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "build123d-mcp": {
      "command": "uv",
      "args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
    }
  }
}

Restart Claude Desktop after saving.

Cursor

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"]
    }
  }
}

VS Code (GitHub Copilot / Continue)

For Continue extension, add to .continue/config.json:

{
  "mcpServers": [
    {
      "name": "build123d-mcp",
      "command": "uv",
      "args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
    }
  ]
}

For GitHub Copilot with MCP support, add to .vscode/mcp.json in your workspace:

{
  "servers": {
    "build123d-mcp": {
      "type": "stdio",
      "command": "uv",
      "args": ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]
    }
  }
}

Codex CLI / Antigravity / Copilot / Cline (AGENTS.md)

These agents read project guidance from AGENTS.md. Add the server to your agent's MCP config with the same launch command as the clients above:

command: uv
args:    ["tool", "run", "--python", "3.12", "build123d-mcp@latest"]

Then install the workflow guidance into AGENTS.md so the agent follows the build → validate() → export() loop:

install_skill(target="agents-md", skill="modeling")   # 3D modeling from a spec / drawing
install_skill(target="agents-md", skill="drawing")    # engineering drawings from geometry

(install_skill also supports target="claude", "cursor", and "windsurf".)

HTTP transport (advanced)

By default the server runs over stdio — one isolated session per client process. With --transport http it serves streamable-HTTP for web/embedded deployments.

⚠️ HTTP mode uses one shared CAD session for every request unless your host installs middleware that sets a per-request WorkerSession on the _session_var contextvar (see http_app()). Do not expose HTTP mode to more than one user without that middleware — they would all read and mutate the same session state.

System prompt

For best results, paste the contents of default_prompt.md as a system prompt in your AI client. This tells the assistant to work incrementally, verify geometry after each step, and use the tools in the right order.

Status

Active development (v0.3.14).