Nano Banana
# @saroby/nanobanana-mcp
Google Gemini image generation MCP server (Nano Banana).
## Features
- **generate_image** - Generate images using Google Gemini models
- `flash` mode: gemini-2.5-flash-image (fast, ~2-3s)
- `pro` mode: gemini-3-pro-image-preview (high quality, ~5-8s)
- **list_images** - List generated images in a directory
## Setup
### Get API Key
1. Go to [Google AI Studio](https://aistudio.google.com/apikey)
2. Create an API key
### Install in Claude Code
```bash
claude mcp add nanobanana -e GEMINI_API_KEY=your-key-here -- npx -y @saroby/nanobanana-mcp
```
### Manual Usage
```bash
GEMINI_API_KEY=your-key-here npx @saroby/nanobanana-mcp
```
## Tools
### generate_image
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `prompt` | string | Yes | Image description (1-8192 chars) |
| `model` | `"flash"` \| `"pro"` | No | Model selection (default: flash) |
| `aspect_ratio` | enum | No | `1:1`, `16:9`, `9:16`, `4:3`, `3:4` |
| `negative_prompt` | string | No | Elements to exclude |
| `output_dir` | string | No | Save directory (default: ./nanobanana-images) |
| `count` | number | No | Number of images 1-4 (default: 1) |
### list_images
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `directory` | string | No | Directory to search (default: ./nanobanana-images) |
## Development
```bash
npm install
npm run build
npm run dev # watch mode
```
## License
MIT
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: generate_image creates new images, while list_images retrieves existing ones. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern (generate_image, list_images) with clear, descriptive names. The naming convention is uniform and predictable throughout the set.
With only two tools, the server feels severely under-scoped for an image generation domain. There are obvious gaps like deleting, updating, or managing images beyond listing, making it difficult for agents to perform complete workflows.
The tool surface is highly incomplete for image generation and management. While it covers creation and listing, it lacks essential operations such as deleting images, updating metadata, or viewing detailed image properties, which will cause agent failures in many scenarios.