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 "Install 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 Related Servers Score Discussions

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: 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 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 installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

1Releases (12mo)

Commit activity

Resources

GitHub Repository

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