mcp-nanobanana-pro 2026.1.24.1

MCP Server for NanoBanana AI Image Generation via AceDataCloud API

MCP NanoBanana

A Model Context Protocol (MCP) server for AI image generation and editing using Google's Nano Banana model through the AceDataCloud API.

Generate and edit AI images directly from Claude, VS Code, or any MCP-compatible client.

Features

• Image Generation - Create high-quality images from text prompts
• Image Editing - Modify existing images or combine multiple images
• Virtual Try-On - Put clothing on people in photos
• Product Placement - Place products in realistic scenes
• Task Tracking - Monitor generation progress and retrieve results

Quick Start

1. Get API Token

Get your API token from AceDataCloud Platform:

1. Sign up or log in
2. Navigate to Nano Banana Images API
3. Click "Acquire" to get your token

2. Install

# Clone the repository
git clone https://github.com/AceDataCloud/MCPNanoBanana.git
cd MCPNanoBanana

# Install with pip
pip install -e .

# Or with uv (recommended)
uv pip install -e .

3. Configure

# Copy example environment file
cp .env.example .env

# Edit with your API token
echo "ACEDATACLOUD_API_TOKEN=your_token_here" > .env

4. Run

# Run the server
mcp-nanobanana-pro

# Or with Python directly
python main.py

Claude Desktop Integration

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "nanobanana": {
      "command": "mcp-nanobanana-pro",
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Or if using uv:

{
  "mcpServers": {
    "nanobanana": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/MCPNanoBanana", "mcp-nanobanana-pro"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Available Tools

Image Generation

Tool	Description
nanobanana_generate_image	Generate an image from a text prompt
nanobanana_edit_image	Edit or combine images with AI

Tasks

Tool	Description
nanobanana_get_task	Query a single task status
nanobanana_get_tasks_batch	Query multiple tasks at once

Usage Examples

Generate Image from Prompt

User: Create an image of a sunset over mountains

Claude: I'll generate that image for you.
[Calls nanobanana_generate_image with detailed prompt]

Virtual Try-On

User: Put this shirt on this model
[Provides two image URLs]

Claude: I'll combine these images.
[Calls nanobanana_edit_image with both image URLs]

Product Photography

User: Place this product in a modern kitchen scene
[Provides product image URL]

Claude: I'll create a product scene for you.
[Calls nanobanana_edit_image with scene description]

Prompt Writing Tips

For best results, include these elements in your prompts:

• Main Subject: What is the primary focus?
• Atmosphere: What mood should the image convey?
• Lighting: How is the scene illuminated?
• Camera/Lens: What photographic style? (85mm portrait, wide-angle, etc.)
• Quality Keywords: Technical descriptors (bokeh, film grain, HDR, etc.)

Example Prompt

A photorealistic close-up portrait of an elderly Japanese ceramicist
with deep wrinkles and a warm smile. Soft golden hour light streaming
through a window. Captured with an 85mm portrait lens, soft bokeh
background. Serene and masterful mood.

Configuration

Environment Variables

Variable	Description	Default
ACEDATACLOUD_API_TOKEN	API token from AceDataCloud	Required
ACEDATACLOUD_API_BASE_URL	API base URL	https://api.acedata.cloud
NANOBANANA_REQUEST_TIMEOUT	Request timeout in seconds	180
LOG_LEVEL	Logging level	INFO

Command Line Options

mcp-nanobanana-pro --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/MCPNanoBanana.git
cd MCPNanoBanana

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

NanoBanana/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for NanoBanana API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   ├── server.py          # MCP server initialization
│   ├── types.py           # Type definitions
│   └── utils.py           # Utility functions
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── image_tools.py     # Image generation/editing tools
│   └── task_tools.py      # Task query tools
├── prompts/                # MCP prompt templates
│   └── __init__.py
├── tests/                  # Test suite
├── .env.example           # Environment template
├── .gitignore
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud NanoBanana API:

• NanoBanana Images API - Image generation and editing
• NanoBanana Tasks API - Task queries

Use Cases

• Portrait Enhancement - Try different clothing on the same person
• Product Scene Composition - Place white-background products in realistic environments
• Attribute Replacement - Change materials, colors, or variants
• Poster Quick Editing - Rapidly change styles or themes
• 2D to 3D Conversion - Convert images to 3D product mockups
• Image Restoration - Restore old or damaged photos

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing)
5. Open a Pull Request

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• Model Context Protocol
• MCP Python SDK

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Made with love by AceDataCloud