3D CAD BasePlate Generator MCP
Generates printable 3D CAD models (e.g., bottle caps) from photos by combining client-side dimension extraction with CadQuery precision modeling, mesh repair, and optional AI-based visual mesh generation via Meshy.
README
3D CAD BasePlate Generator MCP.
A NitroStack MCP server for the bottle-cap-cad workflow: turn a photo of a broken bottle cap, worn plug, or cavity into a printable replacement.
Vision/dimension extraction from the source photo happens client-side
(in the MCP client, e.g. Claude) — none of these tools re-derive dimensions
from pixels. generate_visual_mesh is the one exception, since Meshy infers
geometry directly from the image itself.
Tools
| Tool | Input | Backend | Network |
|---|---|---|---|
generate_precise_cap |
dimensions JSON (outer/inner diameter, height, thread pitch/starts/depth) | CadQuery (Python, local subprocess) | none |
fill |
hole/cavity geometry JSON — circle, rectangle, stadium (slot), polygon, or freeform/organic outline, uniform or tapered | CadQuery (Python, local subprocess) | none |
repair_mesh |
mesh URL or base64 (STL/OBJ/PLY/GLB/OFF) | PyMeshLab (Python, local subprocess) | none (fetches url if given) |
generate_visual_mesh |
raw image (base64) | Meshy image-to-3D API | HTTPS |
A fill-geometry-guide MCP prompt is also registered, walking a caller
through the fields fill expects.
Suggested pipeline: generate_visual_mesh (rough visual reference from
a photo) → repair_mesh (make it manifold/watertight) → generate_precise_cap
or fill for the actual dimensionally-accurate part → repair_mesh again if
the CAD output needs cleanup after boolean operations.
Setup
npm install
pip install -r requirements.txt --break-system-packages # or your own venv
cp .env.example .env # set MESHY_API_KEY
npm run build
npm start
For local iteration without a build step: npm run dev.
generate_precise_cap, fill, and repair_mesh shell out to a local Python
interpreter (python-runtime.ts auto-detects python3/python/py, or
respects a PYTHON_EXECUTABLE override in .env) — Python 3 with the
packages in requirements.txt needs to be installed wherever this server
runs. generate_visual_mesh only needs MESHY_API_KEY; it makes no Python
calls.
Structure
3D-CAD-Modeling-using-MCP/
├── src/
│ ├── app.module.ts # registers the four tool providers + prompt
│ ├── main.ts # bootstrap / entrypoint
│ ├── index.ts # re-exports main.ts
│ ├── modules/bottle-cap-cad/
│ │ └── bottle-cap-cad.prompts.ts # fill-geometry-guide prompt
│ └── tools/
│ ├── shapes.ts # shared circle/rectangle/stadium/polygon/freeform zod schema
│ ├── cap.tools.ts # generate_precise_cap
│ ├── fill.tools.ts # fill
│ ├── repair.tools.ts # repair_mesh
│ ├── mesh.tools.ts # generate_visual_mesh (pure TS, calls Meshy)
│ ├── python-runtime.ts # local python3/python/py auto-detection
│ └── cad-api-client.ts # optional HTTP client for a remote CAD API
├── cad/
│ ├── precise_cap.py # CadQuery threaded-cap builder
│ ├── fill.py # CadQuery plug builder
│ └── repair_mesh.py # PyMeshLab repair pipeline
├── package.json
├── tsconfig.json
└── requirements.txt
Notes on the current code
- Thread generation:
precise_cap.pybuilds a solid, closed-top cap with a blind bore (not a through-hole) and sweeps one or more real helical thread ridges onto the skirt's inner wall along acq.Wire.makeHelix()path.threadStarts(1–4),threadDepth, andtopThicknessare optional fields ongenerate_precise_cap;threadPitchfalls back to an approximate PCO 1810-style pitch (3.18mm, single start) when omitted. This is a parametric approximation tuned for FDM printing, not a certified thread-spec generator. fillshapes:shapes.tssupports five top/bottom shape types —circle,rectangle(with optional corner radius),stadium(true rounded slot with semicircular ends),polygon(straight-edged, arbitrary point count), andfreeform(a smooth closed spline through 6–300 traced boundary points, for organic/irregular openings).capStyle: "flush"is only implemented for circle/rectangle/stadium/freeform tops — a polygon top withflushis downgraded tononebyfill.tools.ts, with a warning returned to the caller rather than failing silently.- Low-confidence inputs: both
generate_precise_capandfillaccept aconfidencefield (high/medium/low, from the caller's own vision-based dimension extraction). Alowvalue still generates output, but the response includes a warning recommending the dimensions be confirmed before printing. - Remote CAD API:
cad-api-client.tssupports routinggenerate_precise_cap/fill/repair_meshover HTTP to aCAD_API_URLinstead of spawning Python locally, for hosting environments without a Python runtime. This repo does not currently include the server-side counterpart (noapi/directory) —CAD_API_URL/CAD_API_KEYare recognized but there's nothing to point them at yet. Leave both unset for local development; the tools fall back to the local-subprocess path automatically. - Meshy polling:
mesh.tools.tspolls Meshy'simage-to-3dendpoint every 3s for up to 60 attempts rather than using a webhook callback. Meshy's free tier output is CC BY 4.0 licensed — check meshy.ai/pricing for current terms before relying on it beyond a demo. app.module.tsregisters a harmless placeholder for NitroStack'sOAUTH_CONFIGDI token purely to silence a noisy (but non-fatal) startup log fromOAuthModule— this server has no HTTP/OAuth surface and the placeholder has no effect on transport selection.
Team
- Reshvanth Yeddla
- Nirlep Boddapally
- Tarun
- Vijay Reddy
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。