Nan Banana

Provides AI-powered image generation via Gemini models with automatic model selection and flexible output controls.

python

144

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "zhongweili-nanobanana-mcp-server": {
      "command": "uvx",
      "args": [
        "nanobanana-mcp-server@latest"
      ],
      "env": {
        "GCP_REGION": "us-central1",
        "GCP_PROJECT_ID": "my-gcp-project",
        "GEMINI_API_KEY": "your-gemini-api-key-here",
        "IMAGE_OUTPUT_DIR": "/path/to/images",
        "NANOBANANA_AUTH_METHOD": "vertex_ai"
      }
    }
  }
}

You can run and use the Nano Banana MCP Server to access AI-powered image generation through Gemini models with smart model selection. This server exposes multiple Gemini models, handles authentication, and provides convenient options for deployment, configuration, and image output control to fit production or development needs.

How to use

You will connect to the Nano Banana MCP Server using an MCP client. Start the server with one of the available runtime commands, then point your MCP client to the local or remote server configuration. The server automatically selects the best Gemini model based on your prompt or explicit model_tier settings, and it supports grounding, multi-object scenes, aspect ratio control, and output file management.

How to install

Prerequisites you need before installation are a Gemini API key and a Python 3.11+ environment for development and testing.

# Prerequisites
- Gemini API key
- Python 3.11+

# Install using the MCP registry (recommended)
uvx nanobanana-mcp-server@latest

# Or install with pip
pip install nanobanana-mcp-server

Additional sections

Configuration and runtime details are provided to help you securely connect, authenticate, and select models. You can run multiple MCP server configurations to fit your environment, including production deployments on Google Cloud with Vertex AI ADC or local development using an API key.

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