mcpField
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Nano Banana Mcp

Nano Banana Mcp

Local
github.com/ConechoAIMedia & Design236Updated 51w ago

About Nano Banana Mcp

Nano Banana Mcp is an MCP server for generating and editing images with Google's Gemini 2.5 Flash Image API. It supports continued edits and reference images, with generated files saved automatically.

Key features

  1. 01Generate images from text prompts
  2. 02Edit image files with prompts
  3. 03Continue editing the latest image
  4. 04Use multiple reference images
  5. 05Save images with organized filenames
  6. 06Configure and check Gemini API credentials

Use cases

  1. 01Create visual assets from text descriptions
  2. 02Revise an image through successive prompts
  3. 03Apply reference-image guidance to an edit
  4. 04Generate mockups and prototypes

MCP support

Provides image generation, editing, configuration, and image-status tools to MCP clients. It uses Google's Gemini 2.5 Flash Image API and requires a Gemini API key.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"Nano-Banana-MCP": {
"command": "npx",
"args": [
"-y",
"nano-banana-mcp"
],
"env": {
"GEMINI_API_KEY": "<GEMINI_API_KEY>"
}
}
}
}
Run commandnpx -y nano-banana-mcp
Environment variables to set
GEMINI_API_KEYSecret

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmnano-banana-mcp

Tools

6

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
apiKeystringYesYour Gemini API key from Google AI Studio

Details

From the project README

Nano-Banana MCP Server 🍌

🤖 This project was entirely generated by Claude Code - an AI coding assistant that can create complete, production-ready applications from scratch.

A Model Context Protocol (MCP) server that provides AI image generation and editing capabilities using Google's Gemini 2.5 Flash Image API. Generate stunning images, edit existing ones, and iterate on your creations with simple text prompts.

Nano-Banana-MCP MCP server

✨ Features

  • 🎨 Generate Images: Create new images from text descriptions
  • ✏️ Edit Images: Modify existing images with text prompts
  • 🔄 Iterative Editing: Continue editing the last generated/edited image
  • 🖼️ Multiple Reference Images: Use reference images for style transfer and guidance
  • 🌍 Cross-Platform: Smart file paths for Windows, macOS, and Linux
  • 🔧 Easy Setup: Simple configuration with API key
  • 📁 Auto File Management: Automatic image saving with organized naming

🔑 Setup

  1. Get your Gemini API key:

  2. Configure the MCP server: See configuration examples for your specific client below (Claude Code, Cursor, or other MCP clients).

💻 Usage with Claude Code

Configuration:

Add this to your Claude Code MCP settings:

Option A: With environment variable (Recommended - Most Secure)

{
  "mcpServers": {
    "nano-banana": {
      "command": "npx",
      "args": ["nano-banana-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Option B: Without environment variable

{
  "mcpServers": {
    "nano-banana": {
      "command": "npx",
      "args": ["nano-banana-mcp"]
    }
  }
}
Usage Examples:
Generate an image of a sunset over mountains
Edit this image to add some birds in the sky
Continue editing to make it more dramatic

🎯 Usage with Cursor

Configuration:

Add to your Cursor MCP configuration:

Option A: With environment variable (Recommended)

{
  "nano-banana": {
    "command": "npx",
    "args": ["nano-banana-mcp"],
    "env": {
      "GEMINI_API_KEY": "your-gemini-api-key-here"
    }
  }
}

Option B: Without environment variable

{
  "nano-banana": {
    "command": "npx",
    "args": ["nano-banana-mcp"]
  }
}
Usage Examples:
  • Ask Cursor to generate images for your app
  • Create mockups and prototypes
  • Generate assets for your projects

🔧 For Other MCP Clients

If you're using a different MCP client, you can configure nano-banana-mcp using any of these methods:

Configuration Methods

Method A: Environment Variable in MCP Config (Recommended)

{
  "nano-banana": {
    "command": "npx",
    "args": ["nano-banana-mcp"],
    "env": {
      "GEMINI_API_KEY": "your-gemini-api-key-here"
    }
  }
}

Method B: System Environment Variable

export GEMINI_API_KEY="your-gemini-api-key-here"
npx nano-banana-mcp

Method C: Using the Configure Tool

npx nano-banana-mcp
# The server will prompt you to configure when first used
# This creates a local .nano-banana-config.json file

🛠️ Available Commands

generate_image

Create a new image from a text prompt.

generate_image({
  prompt: "A futuristic city at night with neon lights"
})
edit_image

Edit a specific image file.

edit_image({
  imagePath: "/path/to/image.png",
  prompt: "Add a rainbow in the sky",
  referenceImages?: ["/path/to/reference.jpg"] // optional
})
continue_editing

Continue editing the last generated/edited image.

continue_editing({
  prompt: "Make it more colorful",
  referenceImages?: ["/path/to/style.jpg"] // optional
})
get_last_image_info

Get information about the last generated image.

get_last_image_info()
configure_gemini_token

Configure your Gemini API key.

configure_gemini_token({
  apiKey: "your-gemini-api-key"
})
get_configuration_status

Check if the API key is configured.

get_configuration_status()

⚙️ Configuration Priority

The MCP server loads your API key in the following priority order:

  1. 🥇 MCP Configuration Environment Variables (Highest Priority)

    • Set in your claude_desktop_config.json or MCP client config
    • Most secure as it's contained within the MCP configuration
    • Example: "env": { "GEMINI_API_KEY": "your-key" }
  2. 🥈 System Environment Variables

    • Set in your shell/system environment
    • Example: export GEMINI_API_KEY="your-key"
  3. 🥉 Local Configuration File (Lowest Priority)

    • Created when using the configure_gemini_token tool
    • Stored as .nano-banana-config.json in current directory
    • Automatically ignored by Git and NPM

💡 Recommendation: Use Method 1 (MCP config env variables) for the best security and convenience.

📁 File Storage

Images are automatically saved to platform-appropriate locations:

  • Windows: %USERPROFILE%\\Documents\\nano-banana-images\\
  • macOS/Linux: ./generated_imgs/ (in current directory)
  • System directories: ~/nano-banana-images/ (when run from system paths)

File naming convention:

  • Generated images: generated-[timestamp]-[id].png
  • Edited images: edited-[timestamp]-[id].png

🎨 Example Workflows

Basic Image Generation
  1. generate_image - Create your base image
  2. continue_editing - Refine and improve
  3. continue_editing - Add final touches
Style Transfer
  1. generate_image - Create base content
  2. edit_image - Use reference images for style
  3. continue_editing - Fine-tune the result
Iterative Design
  1. generate_image - Start with a concept
  2. get_last_image_info - Check current state
  3. continue_editing - Make adjustments
  4. Repeat until satisfied

🔧 Development

This project was created with Claude Code and follows these technologies:

  • TypeScript - Type-safe development
  • Node.js - Runtime environment
  • Zod - Schema validation
  • Google GenAI - Image generation API
  • MCP SDK - Model Context Protocol
Local Development
# Clone the repository
git clone https://github.com/claude-code/nano-banana-mcp.git
cd nano-banana-mcp

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

# Run tests
npm test

📋 Requirements

  • Node.js 18.0.0 or higher
  • Gemini API key from Google AI Studio
  • Compatible with Claude Code, Cursor, and other MCP clients

🤝 Contributing

This project was generated by Claude Code, but contributions are welcome! Please feel free to:

  • Report bugs
  • Suggest new features
  • Submit pull requests
  • Improve documentation

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

  • Claude Code - For generating this entire project
  • Google AI - For the powerful Gemini 2.5 Flash Image API
  • Anthropic - For the Model Context Protocol
  • Open Source Community - For the amazing tools and libraries

📞 Support


✨ Generated with love by Claude Code - The future of AI-powered development is here!

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.