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Nano Banana MCP Server

@ConechoAI

Repository

Basic information

Category

Other

Transports

stdio

Author

ConechoAI

Submitted by

Bin He

About Nano Banana MCP Server

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.

#ai-image-generation#google-s-gemini-2-5-flash-image-api#nano-banana#mcp

Nano Banana MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Nano Banana MCP Server tools

6

Configure your Gemini API token for nano-banana image generation

Generate a NEW image from text prompt. Use this ONLY when creating a completely new image, not when modifying an existing one.

Edit a SPECIFIC existing image file, optionally using additional reference images. Use this when you have the exact file path of an image to modify.

Check if Gemini API token is configured

Continue editing the LAST image that was generated or edited in this session, optionally using additional reference images. Use this for iterative improvements, modifications, or changes to the most recent image. This automatically uses the previous image without needing a file path.

Get information about the last generated/edited image in this session (file path, size, etc.). Use this to check what image is currently available for continue_editing.

Overview

What is Nano Banana Mcp?

Nano Banana Mcp is a Model Context Protocol (MCP) server that provides AI image generation and editing capabilities using Google’s Gemini 2.5 Flash Image API. It lets you create images from text prompts, edit existing images, iterate on results, and use reference images for style transfer.

How to use Nano Banana Mcp?

Configure the server with your Gemini API key via an environment variable in your MCP client config (e.g., GEMINI_API_KEY), a system environment variable, or the built-in configure_gemini_token tool. Then invoke commands such as generate_image, edit_image, continue_editing, get_last_image_info, or get_configuration_status through your MCP client (Claude Code, Cursor, or others).

Key features of Nano Banana Mcp

  • Generate new images from text descriptions
  • Edit existing images with natural language prompts
  • Iteratively edit the last generated/edited image
  • Use multiple reference images for style guidance
  • Cross-platform file handling for Windows, macOS, Linux
  • Automatic image saving with organized naming

Use cases of Nano Banana Mcp

  • Generate visual assets or mockups from text descriptions
  • Edit and refine existing images by describing changes
  • Apply style transfer by providing reference images
  • Iteratively develop a design concept through successive edits

Frequently asked questions

1

What are the dependencies or runtime requirements?

Node.js 18.0.0 or higher and a Gemini API key from Google AI Studio are required.

2

How do I configure my API key?

You can set it as an environment variable in your MCP client config (recommended), as a system environment variable (`GEMINI_API_KEY`), or use the `configure_gemini_token` tool to create a local `.nano-banana-config.json` file.

3

Where are generated images saved?

Images are saved to platform-specific directories: `%USERPROFILE%\Documents\nano-banana-images\` on Windows, `./generated_imgs/` on macOS/Linux, or `~/nano-banana-images/` when run from system paths.

4

What editing features does it support?

You can edit a specific image file with `edit_image`, continue editing the last generated/edited image with `continue_editing`, and optionally supply reference images for style transfer.

5

Is the API key stored securely?

The recommended method keeps the key in your MCP client’s environment config, which is contained within the MCP configuration. System environment variables and a local config file are also supported, with the config file automatically ignored by Git and NPM.

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