3D Agent MCP
Enables local LLM execution for agent tasks without cloud dependency, supporting models like Qwen.
Provides image generation via DALL-E 3 and large language model capabilities for agent orchestration.
Provides access to image generation models like SDXL and Flux for creating 2D previews.
Click on "Install 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., "@3D Agent MCPgenerate a 3D printable model of a coffee mug"
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.
🖨️ 3D Agent MCP
Text → 2D Preview → 3D Model → Print-Ready STL
AI-powered multi-agent pipeline for generating 3D printable models from text descriptions, with MCP server for seamless AI assistant integration.
🇬🇧 English | 🇷🇺 Русский
Demo
Generated Examples
2D previews generated before 3D conversion — faster iteration, less API cost
Multi-View Generation
Multiple camera angles → higher-quality 3D geometry via Hunyuan3D-2mv
Related MCP server: Kiln
Architecture
System Context (C4 Level 1)
Containers (C4 Level 2)
Agent Pipeline
User Prompt
│
▼
┌─────────────────────┐
│ Planner Agent │ ← Decomposes prompt into objects
└─────────┬───────────┘
│
▼
┌─────────────────────┐
│ Image Gen Agent │ ← DALL-E 3 / FLUX / Qwen (2D preview)
└─────────┬───────────┘
│
[User confirms preview]
│
▼
┌─────────────────────┐
│ Generation Agent │ ← Tripo3D API / Hunyuan3D (local)
└─────────┬───────────┘
│
▼
┌─────────────────────────────────────┐
│ Intelligent PostProcessing Agent │
│ ├── Overhang analysis (24 angles) │
│ ├── Support strategy decision │
│ └── Optimal orientation on bed │
└─────────┬───────────────────────────┘
│
▼
Print-Ready STLTechnical Stack
Features
Feature | Description |
Text-to-3D | Generate 3D model from any text description |
2D Preview gate | Create image preview before expensive 3D API call |
Intelligent post-processing | AI agent analyzes geometry, decides supports and orientation |
Multi-view generation | Multiple camera angles → better 3D quality |
Multi-object scenes | Plan and generate complex scenes with multiple objects |
MCP integration | Use from Claude Desktop, Cursor, and any MCP client |
Local models | Hunyuan3D-2, TripoSR, FLUX — no API costs, runs on-premise |
Docker stack | Full local stack with GPU support |
Intelligent Post-Processing Output
desk_organizer analysis:
✅ Printable without supports in recommended orientation.
Complexity: EASY
AI Analysis:
- Geometry complexity: MEDIUM
- Max overhang angle: 38.5°
- Bed contact area: 1 250 mm²
- No internal cavities detected
- Recommended: rotate 180° around X axisQuick Start
Option 0 — Docker image (fastest)
docker pull ghcr.io/teslaproduuction/3d-agent-mcp:latest
cp .env.example .env
# Fill in API keys
docker-compose up -d
# → http://localhost:7860Option 1 — UV (recommended, 10–100× faster than pip)
# Install UV
winget install --id=astral-sh.uv -e # Windows
curl -LsSf https://astral.sh/uv/install.sh | sh # Linux/macOS
# Clone and setup
git clone https://github.com/teslaproduuction/3d-agent-mcp.git
cd 3d-agent-mcp
uv venv --python 3.10
uv sync --all-extras
# Configure
cp .env.example .env
# Edit .env with your API keys
# Run
uv run python ui/gradio_app.py
# → http://localhost:7860Option 2 — Docker (full stack with local models)
cp .env.example .env
# Edit .env
docker-compose up -d --build
# → http://localhostOption 3 — pip
python -m venv .venv
source .venv/bin/activate # Linux/macOS
.venv\Scripts\activate # Windows
pip install -r requirements.txt
cp .env.example .env
python ui/gradio_app.py # → http://localhost:7860MCP Integration
Works with Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.
Claude Desktop config (claude_desktop_config.json)
{
"mcpServers": {
"3d-agent": {
"command": "python",
"args": ["/path/to/3d-agent-mcp/mcp_server/server.py"],
"env": {
"TRIPO_API_KEY": "your_key",
"OPENAI_API_KEY": "your_key"
}
}
}
}Usage in Claude
User: Generate a phone stand for 3D printing
Claude: [calls generate_3d_model tool]
✅ Model generated and optimized for printing!
- File: outputs/models/phone_stand_optimized.stl
- Supports: none required
- Orientation: base-down
- Print time: ~2h 15minAvailable MCP tools: generate_3d_model · generate_2d_preview · analyze_printability · plan_scene
→ See mcp_server/README.md for full API docs.
API Keys
Key | Purpose | Required |
| DALL-E 3 image gen + GPT for agents | For cloud mode |
| 3D generation (Tripo3D cloud) | For cloud mode |
| Claude models as agent LLM | Optional |
| SDXL / Flux image generation | Optional |
No cloud keys needed for local mode — run Hunyuan3D + FLUX via Docker stack.
Configuration
config.yaml controls all behavior:
default_settings:
# Image generation
image_generation:
provider: "local" # local | dalle3 | sdxl | flux
# 3D generation
generation:
api_provider: "local" # local | tripo | meshy
face_limit: 10000
# Post-processing
postprocessing:
mode: "intelligent" # AI decides automatically
auto_orient: true
max_overhang_angle: 45.0
# Printer profile
printer:
build_volume: [220, 220, 250] # mm — Ender 3 / Bambu A1
nozzle_diameter: 0.4
material: "PLA"
# LLM backend
llm:
default_provider: "ollama" # ollama | openai | anthropic
local:
ollama_models: ["qwen2.5:32b", "qwen2.5:7b"]Project Structure
3d-agent-mcp/
├── agents/ # AI agents
│ ├── coordinator.py # Pipeline orchestrator
│ ├── planner_agent.py # Scene decomposition
│ ├── image_generation_agent.py # 2D preview
│ ├── generation_agent.py # 3D API calls
│ └── intelligent_postprocessing_agent.py
│
├── api_clients/ # API wrappers
│ ├── llm_client.py # OpenAI / Anthropic / Ollama
│ ├── image_api_client.py # DALL-E / SDXL / FLUX
│ └── tripo_client.py # Tripo3D
│
├── mcp_server/ # MCP server
│ ├── server.py # Tool definitions
│ └── README.md # MCP API docs
│
├── ui/ # Gradio web UI
│ ├── gradio_app.py # Main app
│ └── tabs/, handlers/, components/
│
├── postprocessing/ # Geometry analysis
├── docker/ # Local model containers
│ ├── hunyuan3d/ # Hunyuan3D-2 (local 3D)
│ ├── flux/ # FLUX.1 (local image gen)
│ ├── comfyui/ # ComfyUI
│ └── nginx/ # Reverse proxy
│
├── tests/
├── config.yaml # Main config
├── .env.example # API key template
├── docker-compose.yml # Full Docker stack
└── pyproject.tomlDiagrams
Diagram | File |
Component | |
Sequence | |
Activity | |
Deployment | |
Classes |
Development
# Run tests
pytest tests/
# Format
black .
# Lint
flake8 .
# Type check
mypy .Roadmap
Meshy API integration
PySLM — physics-based support generation
G-code preview before printing
Printer preset library (Ender 3, Bambu, Prusa)
Export to OBJ, FBX, GLTF
REST API mode (no Gradio dependency)
Contributing
Fork the repo
Create a feature branch:
git checkout -b feature/my-featureCommit changes:
git commit -m "feat: add my feature"Push:
git push origin feature/my-featureOpen a Pull Request
License
MIT © 2026 — see LICENSE
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