Which integrations are available for this server?

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.

How do I use 3D Agent MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@3D Agent MCP generate 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

by teslaproduuction

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

🖨️ 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.

Python License MCP Gradio Docker AutoGen GHCR

🇬🇧 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: openscad-mcp

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 STL

Technical 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 axis

Quick 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:7860

Option 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:7860

Option 2 — Docker (full stack with local models)

cp .env.example .env
# Edit .env

docker-compose up -d --build
# → http://localhost

Option 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:7860

MCP 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 15min

Available 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
`OPENAI_API_KEY`	DALL-E 3 image gen + GPT for agents	For cloud mode
`TRIPO_API_KEY`	3D generation (Tripo3D cloud)	For cloud mode
`ANTHROPIC_API_KEY`	Claude models as agent LLM	Optional
`REPLICATE_API_TOKEN`	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.toml

Diagrams

Diagram	File
Component	docs/PR/diagrams/01_component.png
Sequence	docs/PR/diagrams/02_sequence.png
Activity	docs/PR/diagrams/03_activity_gci.png
Deployment	docs/PR/diagrams/04_deployment.png
Classes	docs/PR/diagrams/05_classes.png

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-feature
Commit changes: git commit -m "feat: add my feature"
Push: git push origin feature/my-feature
Open a Pull Request

License

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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