strands-cad 0.5.1

🔧 CAD, mesh, SDF, cadquery, neural & print tools + WebAuthn-gated 3d printer dashboard for Strands agents - prompt-to-print pipeline

🔧 strands-cad

The prompt-to-print pipeline for AI agents. Atomic CAD, mesh, SDF, cadquery, neural & print tools for Strands agents — plus a WebAuthn-gated live printer dashboard with real-time chamber camera.

Talk to an agent → it designs a part → validates its physics → slices it → sends it to your Bambu Lab printer → and you watch it print from a passkey-protected web page on your phone. All local. All open.

Three ways to look at it

• 🤖 For agent builders — 59 atomic, composable tools over MCP. Drop them into Claude Code / Cursor / Kiro / any Strands agent and your model can model, simulate, slice, and print in one conversation.
• 🖨️ For makers — a headless print farm brain: pip install, point it at your printer, and drive prints + watch the camera from any browser, sealed behind device passkeys (Touch ID / Face ID / YubiKey).
• 🦾 For robotics researchers — design manipulation props (T-blocks, peg boards, grippers), compute print-accurate mass/inertia, spawn a MuJoCo world, and print the physical twin — the exact loop behind strands-labs/robots.

Four independent paths to a printable 3D asset

Path	Tool	Best For
Parametric SCAD	scad_render_stl	Mechanical, brackets, parametric families
B-rep CAD (NURBS)	cq_render_stl / cq_render_step	Fillets, chamfers, engineering-grade parts
SDF / implicit math	sdf_render_stl	Organic, twisted, TPMS, blended surfaces
Neural (text/image → 3D)	neural_text_to_stl	AI generation from prompt or reference photo

All roads lead to STL → 3MF → Bambu Lab → live on your dashboard.

Install

Zero-shot — one command, works on Python 3.10–3.13:

pip install strands-cad

That's it. Core gives you SCAD, CadQuery (B-rep), all mesh/STL/3MF ops, slicing, Bambu printer control, and the MCP server — no build-from-source landmines.

Why "zero-shot" matters here: cadquery pulls in numba, and naive resolvers used to drag in the ancient numba 0.53 / llvmlite 0.36 combo that fails to compile on modern Python. strands-cad pins numba>=0.59 / llvmlite>=0.42 so pip install just… works. First try. Every time.

Optional extras (opt-in, kept out of core so the base install stays lean)

pip install "strands-cad[dashboard]"   # 🖥️ WebAuthn dashboard + live camera
pip install "strands-cad[sim]"         # 🦿 MuJoCo physics simulation
pip install "strands-cad[neural]"      # 🧠 torch (for shap-e text→3D)
pip install "strands-cad[all]"         # everything above

Git-only extras (SDF + neural weights — PyPI forbids git URLs)

python -m strands_cad.install_extras            # both (resolver-safe)
python -m strands_cad.install_extras sdf        # fogleman/sdf → sdf_* tools
python -m strands_cad.install_extras neural     # openai/shap-e → neural_* tools

The helper installs shap-e with --no-deps (its setup.py pins an ancient numba) and then satisfies its real runtime deps — so it installs cleanly on 3.10–3.13.

External system tools (only for those specific tools)

• openscad for scad_* tools — brew install openscad
• bambu-studio for slice_bambu — download from bambulab.com
• ffmpeg for the dashboard camera — bundled via imageio-ffmpeg (no system install)

🖥️ Live Printer Dashboard (WebAuthn + chamber camera)

A single command turns your host into a passkey-sealed cockpit for your Bambu printer: live 1080p chamber camera, temps / progress / AMS telemetry, and pause / resume / stop — all behind WebAuthn so a stranger on your LAN can't start fires or move motors.

pip install "strands-cad[dashboard]"

BAMBU_IP=192.168.1.164 BAMBU_ACCESS_CODE=xxxxxxxx \
  strands-cad-dashboard --tls          # → https://localhost:8099

Or let an agent spin it up on demand (also exposed over MCP):

dashboard_start(ip="192.168.1.164", access_code="xxxxxxxx", tls=True)
# → open the URL, tap "Create passkey", and you're watching the print.

How the security model works

1. First visit → the dashboard is unsealed; you enroll an admin passkey (Touch ID / Face ID / Windows Hello / a hardware key). The private key never leaves your device secure enclave — the server only stores the public key.
2. From then on it's sealed: every /api/* call and the camera stream require a valid short-lived JWT session, minted only by proving your passkey.
3. No passwords, nothing to phish, no cloud — 100% on your LAN.

Why TLS? WebAuthn only runs in a "secure context" (HTTPS or localhost). --tls mints a self-signed cert (SANs for every LAN IP + hostname) so passkeys work when you open the dashboard from your phone at https://192.168.1.x:8099. Have mkcert installed? It's auto-detected for a zero-warning trusted cert.

The hard part we solved for you — the Bambu camera. Bambu P1/A1 printers serve the chamber cam only as RTSPS (RTSP-over-TLS) H.264 on port 322, behind LIVE555 with digest auth — and ffmpeg plain -i rtsps://… hangs on it. strands-cad does the RTSP/TLS handshake in pure Python, reassembles the H.264 NAL stream from interleaved RTP, and pipes it to a bundled static ffmpeg for MJPEG — so /api/camera/stream "just works" with a shared frame across all viewers, auto-reconnect, and a single held live-view session. (Verified live: 1920×1080 @ 15 fps.)

Dashboard env var	Default	Meaning
BAMBU_IP	—	printer LAN IP
BAMBU_ACCESS_CODE	—	LAN access code (printer Settings → Network)
STRANDS_CAD_TLS	false	serve HTTPS (needed for LAN passkeys)
STRANDS_CAD_DASH_PORT	8099	dashboard port
STRANDS_CAD_AUTH_ENABLED	true	master WebAuthn switch
STRANDS_CAD_AUTH_RP_ID	derived	pin the WebAuthn relying-party id (a hostname)
STRANDS_CAD_AUTH_BOOTSTRAP	—	one-time secret to gate the first enrollment

The 59 Atomic Tools

Layer	Tools
SCAD (parametric)	scad_probe, scad_render_stl, scad_render_png, scad_validate, scad_view, scad_turntable
G-code	gcode_check, gcode_preview_png
CadQuery (B-rep, NURBS)	cq_render_stl, cq_render_step, cq_import_step, cq_render_svg
SDF (implicit math)	sdf_render_stl, sdf_list_primitives, sdf_gyroid_infill, sdf_from_function, sdf_lattice_infill_stl
Neural (AI generation)	neural_text_to_stl, neural_image_to_stl
Point Cloud	pointcloud_from_stl, pointcloud_to_stl, pointcloud_downsample
STL / Mesh	stl_parse, stl_volume, stl_bbox, stl_weight, stl_repair, stl_transform, stl_convert, mesh_decimate, mesh_normalize, mesh_boolean, mesh_combine, mesh_hollow
3MF	mf3_pack, mf3_unpack, mf3_read_metadata
Slice	slice_bambu, slice_profile_get, slice_estimate
Bambu Printer	bambu_connect, bambu_send, bambu_upload, bambu_status, bambu_control, bambu_camera, bambu_ams
Sim	sim_build_mjcf, sim_run_headless, sim_view_live, sim_inertia_from_stl
Preview	preview_serve, preview_stop
Meta	bom_parse, bom_total, journal_append
Dashboard (WebAuthn + camera)	dashboard_start, dashboard_stop, dashboard_status

MCP Server (Claude Code, Claude Desktop, Cursor, Kiro)

All 59 tools are exposable over the Model Context Protocol via the strands-cad-mcp entrypoint (built on strands-mcp-server).

Claude Code

claude mcp add strands-cad -- strands-cad-mcp
# or skip heavy groups for faster startup:
claude mcp add strands-cad -- strands-cad-mcp --skip neural,sim

Claude Desktop

{
  "mcpServers": {
    "strands-cad": {
      "command": "strands-cad-mcp",
      "args": ["--skip", "neural"]
    }
  }
}

HTTP mode (multi-client / remote)

strands-cad-mcp --http --port 8000            # → http://localhost:8000/mcp
strands-cad-mcp --http --port 8000 --stateless  # multi-node scalable

Options

Flag	Effect
(default)	stdio transport, all available tool groups
--http --port N	StreamableHTTP transport instead of stdio
--stateless	Fresh transport per request (horizontal scaling)
--tools a,b,c	Expose only named tools
--skip neural,sim,...	Skip tool groups (scad,stl,mf3,slice,bambu,sim,preview,meta,sdf,cadquery,neural,dashboard)
--agent-invocation	Also expose invoke_agent for full conversations
--debug	Verbose logging (stderr)

Missing optional deps (torch, mujoco, cadquery, sdf) auto-disable their group — the server always boots with whatever is installed.

🎨 What Can You Render? — Full Showcase

T-block (Push-T RL benchmark)	M4 bracket (CadQuery)	Peg-in-hole board (robot training)	3D text (mesh_from_text)
Twisted torus (SDF .twist())	CSG classic (SDF booleans)	Wavy sphere (custom f(p) math)	Gyroid TPMS lattice

Every image above was generated by the code snippets below — nothing hand-modeled.

Every example below was actually executed with these tools (timings from an M-series Mac). Copy-paste any snippet.

1. Parametric Engineering — CadQuery (B-rep / NURBS)

Object	What It Teaches	Code	Verified Output
Calibration cube	Boxes, chamfers	cq.Workplane("XY").box(20,20,20).edges().chamfer(0.8)	2.2 KB STL in 0.01s
Mounting bracket	Fillets, hole patterns, M4 clearance	see below	150 KB STL, 0% overhangs, fits X1C bed
Peg-in-hole board	pushPoints, multi-hole ops — robot manipulation training	see below	100 KB STL
T-block ("push-T" benchmark)	union, robot RL objects	see below	51 KB STL, 18.05 g PLA

# T-block — the classic "Push-T" manipulation benchmark object
cq_render_stl(script='''
result = (
    cq.Workplane("XY").box(80, 20, 15)                         # horizontal bar
    .union(cq.Workplane("XY").center(0, -30).box(20, 40, 15))  # vertical stem
    .edges("|Z").fillet(2)
)
''', output_stl="t_block.stl")

# Engineering bracket with M4 mounting holes + center bore
cq_render_stl(script='''
result = (
    cq.Workplane("XY").box(60, 40, 6)
    .edges("|Z").fillet(6)
    .faces(">Z").workplane()
    .rect(46, 26, forConstruction=True).vertices().hole(4.2)   # M4 clearance × 4
    .faces(">Z").workplane().hole(20)                          # center bore
)
''', output_stl="bracket.stl")

# Peg-in-hole board (3 tolerance sizes) — grasping/insertion training
cq_render_stl(script='''
board = cq.Workplane("XY").box(120, 50, 12).edges("|Z").fillet(4)
result = (board.faces(">Z").workplane()
    .pushPoints([(-40, 0)]).hole(10.4)
    .pushPoints([(0, 0)]).hole(15.4)
    .pushPoints([(40, 0)]).hole(20.4))
''', output_stl="peg_board.stl")

Need exact geometry for Fusion/SolidWorks/CNC? Swap to cq_render_step — same script, lossless STEP out. cq_render_svg gives you drawing projections for docs.

2. Implicit Math — SDF (organic, twisted, impossible-in-CAD)

Object	What It Teaches	Expression	Verified Output
Twisted torus	.twist() warp operator	torus(30, 8).twist(radians(180)/60)	3.7 MB STL in 0.3s
CSG classic	Booleans as & - | operators	sphere(20) & box(30) - cylinder(10).orient(X) - cylinder(10).orient(Y) - cylinder(10)	2.5 MB STL in 0.6s
Wavy sphere	Arbitrary f(p)→d math via sdf_from_function	see below	2.6 MB STL in 0.2s
Gyroid lattice	TPMS structures (strong + light)	sdf_gyroid_infill(size=(40,40,40), period=12, thickness=1.6)	40mm cube in 0.5s

# Any math you can write becomes a solid:
sdf_from_function(
    function_source='''
def f(p):
    x, y, z = p[:,0], p[:,1], p[:,2]
    return np.sqrt(x**2 + y**2 + z**2) - 20 + 2.5*np.sin(x*0.6)*np.sin(y*0.6)*np.sin(z*0.6)
''',
    output_stl="wavy_sphere.stl",
    bounds=[-30,-30,-30, 30,30,30],
)

# Fill ANY existing STL interior with gyroid/schwarz-p/diamond lattice:
sdf_lattice_infill_stl(input_stl="bracket.stl", output_stl="bracket_light.stl",
                       lattice="gyroid", period=10, shell_thickness=2)

Run sdf_list_primitives() to see all ~60 primitives/operators (sphere, capsule, rounded_box, twist, bend, shell, dilate, erode, smooth blends…).

3. Neural Generation — text/image → 3D (Shap-E)

neural_text_to_stl(prompt="a stylized rocket ship", output_stl="rocket.stl", steps=64)
neural_image_to_stl(image_path="reference.jpg", output_stl="from_photo.stl")

First call downloads ~1 GB weights; runs on MPS/CUDA/CPU. Great for concept props and organic shapes no one wants to model by hand.

4. 2D → 3D — text, logos, images

Tool	Input	Output
mesh_from_text	"STRANDS", any system font	Extruded nameplate STL (verified: 253 KB)
mesh_from_svg	Logo .svg	Extruded badge/profile
mesh_from_image	Photo (grayscale)	Lithophane / relief heightmap

5. Robot Training Objects — the strands-labs/robots workflow

Generate physical props for RL/manipulation research, then simulate them before printing:

# 1. Design the object (T-block, peg board, cubes — see §1)
# 2. Get real physical properties (PLA @ 15% infill):
sim_inertia_from_stl(stl_file="t_block.stl", material="PLA")
#    → mass=18.05 g, COM, full inertia tensor

# 3. Build a MuJoCo world with correct mass/inertia:
sim_build_mjcf(meshes=[{"name": "t_block", "path": "t_block.stl",
                        "mass_g": 18.05, "pos": [0, 0, 0.05]}],
               output_mjcf="world.xml")

# 4. Simulate headless (verified: 500 steps / 1.0s sim):
sim_run_headless(mjcf_file="world.xml", duration_sec=1.0)
# ... or watch it live:
sim_view_live(mjcf_file="world.xml")

# 5. Synthetic scan data for perception training:
pointcloud_from_stl(stl_file="t_block.stl", output_xyz="scan.xyz", n_points=5000)
pointcloud_to_stl(pointcloud_file="scan.xyz", output_stl="reconstructed.stl")  # closes the loop

Same pipeline powers parts for strands-labs/robots — rover mounts, drone frames, gripper fingers: design → validate mass/inertia → simulate → print.

Runnable end-to-end script: examples/robot_training_props.py builds 5 props (T-block 18.05g, peg board 32.47g, graded 30/40/50mm cube set), computes print-accurate inertia for each, emits one MJCF world, sim-sanity-runs it, and packs a print plate — ~5s, verified. Comments show how to author a strands-robots declarative benchmark (e.g. push_t_to_goal for an SO-101 arm) on the exact props you print.

6. Verify Before You Print

Every mesh gets a free QA pass (all verified on the assets above):

stl_printability("bracket.stl", printer="X1C")   # → fits bed, 0% overhangs, no supports
stl_weight("t_block.stl", material="PLA")        # → 18.05 g @ 15% infill
stl_orient("part.stl", "oriented.stl")           # auto-rotate to minimize supports
stl_check_clearance("peg.stl", "hole.stl")       # 0.2mm tight / 0.4mm loose FDM fits
mesh_decimate("gyroid.stl", "lite.stl", target_faces=80_000)  # 420k → 80k faces (19%)
stl_repair("broken.stl", "fixed.stl")            # fill holes, fix normals, watertight
mesh_hollow("statue.stl", "shell.stl", wall_thickness=2, drain_hole_diameter=4)

7. Plate → Slice → Print (Bambu Lab, fully closed loop)

# Pack multiple parts on one plate (groups = multi-material assemblies):
mf3_pack(items=[
    {"stl": "t_block.stl", "name": "T-block", "position": [0, 0, 0]},
    {"stl": "calibration_cube.stl", "name": "cube", "position": [80, 0, 0]},
], output_3mf="plate.3mf", title="robot training objects")

slice_bambu(input_3mf="plate.3mf", output_gcode="plate.gcode",
            printer_model="Bambu Lab X1 Carbon", profile="PLA_0_20")
gcode_check("plate.gcode")     # verified: PASS — nozzle≤200°C, bed≤35°C, bounds OK
slice_estimate("plate.gcode")  # verified: T-block + cube plate = 2h18m print time

bambu_connect(ip="192.168.1.x", access_code="...", serial="01P00A...")
bambu_ams()                                       # check loaded filament
bambu_upload(file_path="plate.gcode")             # FTPS → SD card
bambu_send(file_path="plate.gcode")               # start the job
bambu_status(); bambu_camera()                    # watch progress + chamber cam

Cheat Sheet — Which Path When?

You want…	Use	Why
Brackets, mounts, enclosures	CadQuery	Fillets/chamfers/holes are first-class
Parametric families (-D overrides)	OpenSCAD	scad_render_stl(defines={"W": 50})
Twisted / blended / organic	SDF	Warps & smooth booleans are free
Lightweight functional parts	SDF gyroid infill	TPMS = max stiffness/gram
"Make me a dragon"	Neural	Text/image → mesh
Logos, nameplates, lithophanes	mesh_from_text / svg / image	2D → extrusion
RL / manipulation props	CadQuery + sim_*	Design + physics validation
Vendor STEP files	cq_import_step	STEP → STL for slicing
See it before printing	scad_view / scad_turntable	Agent receives actual pixels

Quick Examples

Parametric CAD (CadQuery)

from strands_cad import cq_render_stl

cq_render_stl(script='''
result = (
    cq.Workplane("XY").box(60, 40, 5)
    .edges().fillet(2)
    .faces(">Z").hole(20)
)
''', output_stl="bracket.stl")

Implicit Math (SDF)

from strands_cad import sdf_render_stl

sdf_render_stl(
    expression="torus(30, 8).twist(radians(180)/60)",
    output_stl="twisted_torus.stl",
    resolution=0.4,
)

AI Text → 3D (Shap-E)

from strands_cad import neural_text_to_stl

neural_text_to_stl(
    prompt="a stylized rocket ship",
    output_stl="rocket.stl",
    steps=64,
)

Full agent

from strands import Agent
from strands_cad import ALL_TOOLS

agent = Agent(tools=ALL_TOOLS)
agent("Generate a mechanical bracket with M4 mounting holes, "
      "verify weight in PLA, and pack it for my Bambu P1S.")

Design Principles

• Atomic — one tool = one verb = one input shape = one output shape
• No orchestration inside tools; the agent composes
• No hidden state except the Bambu MQTT connection handle
• Standard response: {status, content: [{text}], ...extras}

License

MIT