Forge
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Forgecreate a phone stand model"
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.
Forge
A local MCP server that turns an AI agent into a parametric CAD operator. The agent describes intent ("a phone stand", "make it taller", "add four M3 holes") and Forge turns that into a typed, validated, replayable operation graph that compiles to real, printable CAD geometry (STL/STEP) plus rendered previews.
The LLM never generates executable Python — it emits JSON conforming to a
Pydantic schema. Forge validates it, then compiles it to CadQuery
internally. Every model is persisted as JSON on disk and keyed by model_id,
so edits across turns are deterministic changes to a saved structure.
Requirements
Python 3.12 (not 3.14 — CadQuery/OCP and PyVista/VTK wheels lag new CPython releases). The pin lives in
.python-versionandpyproject.toml.uv.
Related MCP server: openscad-mcp
Install
uv python install 3.12 # if you don't have 3.12
uv sync
uv run python -c "import cadquery" # sanity-check the native stack resolvedRun
uv run forgeThe server speaks MCP over stdio and blocks until the client disconnects (Ctrl-C to exit).
Connect from an MCP client
Point your client (Claude Code, Cursor, etc.) at the forge command. Example
stdio config:
{
"mcpServers": {
"forge": {
"command": "uv",
"args": ["run", "forge"],
"cwd": "/path/to/forge"
}
}
}Tools
All tools take and return an explicit model_id. Failures come back as
structured {"ok": false, "error": {"code", "detail"}} payloads (never raised
exceptions), so an agent can read and recover.
Tool | Purpose | Key output |
| Create a model from an initial operation graph. |
|
| Edit the graph: |
|
| Render an isometric PNG (also returned inline for vision-capable agents). |
|
| Printability report (watertight, positive volume, bed fit). |
|
| Exact BREP metrics. |
|
| Write |
|
Operations
The graph is an ordered list; op #1 must be a primitive. Supported ops:
box, cylinder, sphere, translate, boolean (union/cut/intersect),
fillet, chamfer, hole. Units are millimeters everywhere.
Example — a phone stand:
[
{ "id": "op1", "type": "box", "width": 80, "depth": 60, "height": 8 },
{ "id": "op2", "type": "box", "width": 80, "depth": 10, "height": 70 },
{ "id": "op3", "type": "fillet", "radius": 2.0, "edges": "all" }
]Workspace
Models and artifacts live under a workspace directory, one subdir per model:
<workspace>/<model_id>/
model.json # the operation graph — the single source of truth
preview.png # from preview_model
model.stl # from export_model
model.stepThe workspace defaults to ./forge_workspace/ and is overridable via the
FORGE_WORKSPACE environment variable.
Headless rendering
preview_model uses PyVista/VTK offscreen. On macOS this works natively. On
Linux (e.g. CI) VTK needs a GL context — install libgl1 and run under
xvfb-run, or call pyvista.start_xvfb() before rendering.
Development
uv run pytest -qTests mirror modules 1:1 plus tests/test_tools.py (tool-level integration)
and a golden determinism snapshot in tests/golden/.
This server cannot be deployed
Maintenance
Related MCP Connectors
- OwlCADOAuthcom.owlcad
Parametric 3D CAD for AI agents: build print-ready parts, check them, export STL, 3MF or STEP.
Generate game-ready 3D models, textures, and audio from natural language, over MCP.
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
MCP server for building and testing AI agents with multi-model experimentation and insights.
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