NanoBananaMCP
# NanoBananaMCP
<!-- mcp-name: io.github.AceDataCloud/mcp-nanobanana-pro -->
[](https://pypi.org/project/mcp-nanobanana-pro/)
[](https://pypi.org/project/mcp-nanobanana-pro/)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://modelcontextprotocol.io)
A [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server for AI image generation and editing using [Google's Nano Banana](https://deepmind.google/technologies/imagen/) model through the [AceDataCloud API](https://platform.acedata.cloud).
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
## Tool Reference
| Tool | Description |
|------|-------------|
| `nanobanana_generate_image` | Generate an AI image from a text prompt using Google's Nano Banana model. |
| `nanobanana_edit_image` | Edit or combine images using AI based on a text prompt. |
| `nanobanana_get_task` | Query the status and result of an image generation or edit task. |
| `nanobanana_get_tasks_batch` | Query multiple image generation/edit tasks at once. |
## Quick Start
### 1. Get Your API Token
1. Sign up at [AceDataCloud Platform](https://platform.acedata.cloud)
2. Go to the [API documentation page](https://platform.acedata.cloud/documents/nano-banana-images)
3. Click **"Acquire"** to get your API token
4. Copy the token for use below
### 2. Use the Hosted Server (Recommended)
AceDataCloud hosts a managed MCP server — **no local installation required**.
**Endpoint:** `https://nanobanana.mcp.acedata.cloud/mcp`
All requests require a Bearer token. Use the API token from Step 1.
#### Claude.ai
Connect directly on [Claude.ai](https://claude.ai) with OAuth — **no API token needed**:
1. Go to Claude.ai **Settings → Integrations → Add More**
2. Enter the server URL: `https://nanobanana.mcp.acedata.cloud/mcp`
3. Complete the OAuth login flow
4. Start using the tools in your conversation
#### Claude Desktop
Add to your config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Cursor / Windsurf
Add to your MCP config (`.cursor/mcp.json` or `.windsurf/mcp.json`):
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### VS Code (Copilot)
Add to your VS Code MCP config (`.vscode/mcp.json`):
```json
{
"servers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
Or install the [Ace Data Cloud MCP extension](https://marketplace.visualstudio.com/items?itemName=acedatacloud.acedatacloud-mcp) for VS Code, which registers the hosted MCP servers with one-click setup.
#### JetBrains IDEs
1. Go to **Settings → Tools → AI Assistant → Model Context Protocol (MCP)**
2. Click **Add** → **HTTP**
3. Paste:
```json
{
"mcpServers": {
"nanobanana": {
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Claude Code
Claude Code supports MCP servers natively:
```bash
claude mcp add nanobanana --transport http https://nanobanana.mcp.acedata.cloud/mcp \
-h "Authorization: Bearer YOUR_API_TOKEN"
```
Or add to your project's `.mcp.json`:
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Cline
Add to Cline's MCP settings (`.cline/mcp_settings.json`):
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Amazon Q Developer
Add to your MCP configuration:
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Roo Code
Add to Roo Code MCP settings:
```json
{
"mcpServers": {
"nanobanana": {
"type": "streamable-http",
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
```
#### Continue.dev
Add to `.continue/config.yaml`:
```yaml
mcpServers:
- name: nanobanana
type: streamable-http
url: https://nanobanana.mcp.acedata.cloud/mcp
headers:
Authorization: "Bearer YOUR_API_TOKEN"
```
#### Zed
Add to Zed's settings (`~/.config/zed/settings.json`):
```json
{
"language_models": {
"mcp_servers": {
"nanobanana": {
"url": "https://nanobanana.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_TOKEN"
}
}
}
}
}
```
#### cURL Test
```bash
# Health check (no auth required)
curl https://nanobanana.mcp.acedata.cloud/health
# MCP initialize
curl -X POST https://nanobanana.mcp.acedata.cloud/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json" \
-H "Authorization: Bearer YOUR_API_TOKEN" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'
```
### 3. Or Run Locally (Alternative)
If you prefer to run the server on your own machine:
```bash
# Install from PyPI
pip install mcp-nanobanana-pro
# or
uvx mcp-nanobanana-pro
# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"
# Run (stdio mode for Claude Desktop / local clients)
mcp-nanobanana-pro
# Run (HTTP mode for remote access)
mcp-nanobanana-pro --transport http --port 8000
```
#### Claude Desktop (Local)
```json
{
"mcpServers": {
"nanobanana": {
"command": "uvx",
"args": ["mcp-nanobanana-pro"],
"env": {
"ACEDATACLOUD_API_TOKEN": "your_token_here"
}
}
}
}
```
#### Docker (Self-Hosting)
```bash
docker pull ghcr.io/acedatacloud/mcp-nanobanana-pro:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-nanobanana-pro:latest
```
Clients connect with their own Bearer token — the server extracts the token from each request's `Authorization` header.
## 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` |
| `ACEDATACLOUD_OAUTH_CLIENT_ID` | OAuth client ID (hosted mode) | — |
| `ACEDATACLOUD_PLATFORM_BASE_URL` | Platform base URL | `https://platform.acedata.cloud` |
| `NANOBANANA_REQUEST_TIMEOUT` | Request timeout in seconds | `1800` |
| `LOG_LEVEL` | Logging level | `INFO` |
### Command Line Options
```bash
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
```bash
# Clone repository
git clone https://github.com/AceDataCloud/NanoBananaMCP.git
cd NanoBananaMCP
# 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
```bash
# 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
```bash
# Format code
ruff format .
# Lint code
ruff check .
# Type check
mypy core tools
```
### Build & Publish
```bash
# 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
├── deploy/ # Deployment configs
│ └── production/
│ ├── deployment.yaml
│ ├── ingress.yaml
│ └── service.yaml
├── .env.example # Environment template
├── .gitignore
├── Dockerfile # Docker image for HTTP mode
├── docker-compose.yaml # Docker Compose config
├── LICENSE
├── main.py # Entry point
├── pyproject.toml # Project configuration
└── README.md
```
## API Reference
This server wraps the [AceDataCloud NanoBanana API](https://platform.acedata.cloud/documents/nano-banana-images):
- [NanoBanana Images API](https://platform.acedata.cloud/documents/nano-banana-images) - Image generation and editing
- [NanoBanana Tasks API](https://platform.acedata.cloud/documents/nano-banana-tasks) - 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
## Documentation
<!-- canonical-documentation -->
[Documentation](https://platform.acedata.cloud/documents/nano-banana-mcp)
## License
MIT License - see [LICENSE](LICENSE) for details.
## Links
- [AceDataCloud Platform](https://platform.acedata.cloud)
- [Model Context Protocol](https://modelcontextprotocol.io)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
---
Made with love by [AceDataCloud](https://platform.acedata.cloud)
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: generate_image creates new images from prompts, edit_image modifies or combines existing images, get_task retrieves single task results, and get_tasks_batch retrieves multiple task results. The descriptions reinforce these distinct roles, making tool selection unambiguous.
All tools follow a consistent snake_case pattern with the prefix 'nanobanana_' followed by a clear verb_noun combination (edit_image, generate_image, get_task, get_tasks_batch). The naming convention is perfectly uniform across all four tools, making them predictable and easy to understand.
Four tools is well-scoped for an image generation/editing server, covering core operations: generation, editing, and status retrieval (both single and batch). Each tool earns its place without redundancy, providing a complete yet manageable surface for the domain.
The tool set covers the essential lifecycle of image tasks: create (generate_image), modify (edit_image), and retrieve results (get_task and get_tasks_batch). A minor gap exists in lacking explicit deletion or management tools for tasks, but agents can work around this as the core workflows are fully supported.