Nano Banana MCP Server 🍌

A production-ready Model Context Protocol (MCP) server that provides AI-powered image generation capabilities through Google's Gemini models with intelligent model selection.

⭐ NEW: Nano Banana 2 — Gemini 3.1 Flash Image! 🍌🚀

Nano Banana 2 (gemini-3.1-flash-image-preview) is now the default model — delivering Pro-level quality at Flash speed:

• 🍌 Flash Speed + 4K Quality: Up to 3840px at Gemini 2.5 Flash latency
• 🌐 Google Search Grounding: Real-world knowledge for factually accurate images
• 🎯 Subject Consistency: Up to 5 characters and 14 objects per scene
• ✍️ Precision Text Rendering: Crystal-clear text placement in images
• 🏆 Gemini 3 Pro Image still available for maximum reasoning depth

✨ Features

• 🎨 Multi-Model AI Image Generation: Three Gemini models with intelligent automatic selection
• 🍌 Gemini 3.1 Flash Image (NB2): Default model — 4K resolution at Flash speed with grounding
• 🏆 Gemini 3 Pro Image: Maximum reasoning depth for the most complex compositions
• ⚡ Gemini 2.5 Flash Image: Legacy Flash model for high-volume rapid prototyping
• 🤖 Smart Model Selection: Automatically routes to NB2 or Pro based on your prompt
• 📐 Aspect Ratio Control ⭐ NEW: Specify output dimensions (1:1, 16:9, 9:16, 21:9, and more)
• 📋 Smart Templates: Pre-built prompt templates for photography, design, and editing
• 📁 File Management: Upload and manage files via Gemini Files API
• 🔍 Resource Discovery: Browse templates and file metadata through MCP resources
• 🛡️ Production Ready: Comprehensive error handling, logging, and validation
• ⚡ High Performance: Optimized architecture with intelligent caching

🚀 Quick Start

Prerequisites

1. Google Gemini API Key - Get one free here
2. Python 3.11+ (for development only)

Installation

Option 1: From MCP Registry (Recommended) This server is available in the Model Context Protocol Registry. Search for "nanobanana" or use the MCP name below with your MCP client.

mcp-name: io.github.zhongweili/nanobanana-mcp-server

Option 2: Using uvx

uvx nanobanana-mcp-server@latest

Option 3: Using pip

pip install nanobanana-mcp-server

🔧 Configuration

Authentication Methods

Nano Banana supports two authentication methods via NANOBANANA_AUTH_METHOD:

1. API Key (api_key): Uses GEMINI_API_KEY. Best for local development and simple deployments.
2. Vertex AI ADC (vertex_ai): Uses Google Cloud Application Default Credentials. Best for production on Google Cloud (Cloud Run, GKE, GCE).
3. Automatic (auto): Defaults to API Key if present, otherwise tries Vertex AI.

#### 1. API Key Authentication (Default)

Set GEMINI_API_KEY environment variable.

#### 2. Vertex AI Authentication (Google Cloud)

Required environment variables:

• NANOBANANA_AUTH_METHOD=vertex_ai (or auto)
• GCP_PROJECT_ID=your-project-id
• GCP_REGION=global (default; required for Gemini 3 Pro Image and NB2. Use us-central1 only for the legacy 2.5 Flash Image model.)

Prerequisites:

• Enable Vertex AI API: gcloud services enable aiplatform.googleapis.com
• Grant IAM Role: roles/aiplatform.user to the service account.

Claude Desktop

#### Option 1: Using Published Server (Recommended)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "nanobanana": {
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

#### Option 2: Using Local Source (Development)

If you are running from source code, point to your local installation:

{
  "mcpServers": {
    "nanobanana-local": {
      "command": "uv",
      "args": ["run", "python", "-m", "nanobanana_mcp_server.server"],
      "cwd": "/absolute/path/to/nanobanana-mcp-server",
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

#### Option 3: Using Vertex AI (ADC)

To authenticate with Google Cloud Application Default Credentials (instead of an API Key):

{
  "mcpServers": {
    "nanobanana-adc": {
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "NANOBANANA_AUTH_METHOD": "vertex_ai",
        "GCP_PROJECT_ID": "your-project-id",
        "GCP_REGION": "global"
      }
    }
  }
}

Configuration file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Claude Code (VS Code Extension)

Install and configure in VS Code:

1. Install the Claude Code extension
2. Open Command Palette (Cmd/Ctrl + Shift + P)
3. Run "Claude Code: Add MCP Server"
4. Configure:

   {
     "name": "nanobanana",
     "command": "uvx",
     "args": ["nanobanana-mcp-server@latest"],
     "env": {
       "GEMINI_API_KEY": "your-gemini-api-key-here"
     }
   }

Cursor

Add to Cursor's MCP configuration:

{
  "mcpServers": {
    "nanobanana": {
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

OpenAI Codex

Add to ~/.codex/config.toml (global) or .codex/config.toml (project-scoped):

[mcp_servers.nanobanana]
command = "uvx"
args = ["nanobanana-mcp-server@latest"]

[mcp_servers.nanobanana.env]
GEMINI_API_KEY = "your-gemini-api-key-here"

Or add via the CLI:

codex mcp add

Codex supports both the CLI and VSCode extension using the same config.toml. Once added, Codex can call generate_image, edit_image, and upload_file tools directly in your coding sessions.

Note: The Codex config file is shared by the CLI and the IDE extension. A TOML syntax error will break both simultaneously, so validate your edits carefully.

Continue.dev (VS Code/JetBrains)

Add to your config.json:

{
  "mcpServers": [
    {
      "name": "nanobanana",
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  ]
}

Open WebUI

Configure in Open WebUI settings:

{
  "mcp_servers": {
    "nanobanana": {
      "command": ["uvx", "nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Gemini CLI / Generic MCP Client

# Set environment variable
export GEMINI_API_KEY="your-gemini-api-key-here"

# Run server in stdio mode
uvx nanobanana-mcp-server@latest

# Or with pip installation
python -m nanobanana_mcp_server.server

🤖 Model Selection

Nano Banana supports three Gemini models with intelligent automatic selection:

🍌 NB2 — Nano Banana 2 (Gemini 3.1 Flash Image) ⭐ DEFAULT

Flash speed with Pro-level quality — the best of both worlds

• Quality: Production-ready 4K output
• Resolution: Up to 4K (3840px)
• Speed: ~2-4 seconds per image (Flash-class latency)
• Special Features:
• 🌐 Google Search Grounding: Real-world knowledge for factually accurate images
• 🎯 Subject Consistency: Up to 5 characters and 14 objects per scene
• ✍️ Precision Text Rendering: Clear, well-placed text in images
• Best for: Almost everything — production assets, marketing, photography, text overlays
• model_tier: "nb2" (or "auto" — NB2 is the auto default)

🏆 Pro Model — Nano Banana Pro (Gemini 3 Pro Image)

Maximum reasoning depth for the most demanding compositions

• Quality: Highest available
• Resolution: Up to 4K (3840px)
• Speed: ~5-8 seconds per image
• Special Features:
• 🧠 Advanced Reasoning: Configurable thinking levels (LOW/HIGH)
• 🌐 Google Search Grounding: Real-world knowledge integration
• 📐 Media Resolution Control: Fine-tune vision processing detail
• Best for: Complex narrative scenes, intricate compositions, maximum reasoning required
• model_tier: "pro"

⚡ Flash Model (Gemini 2.5 Flash Image)

Legacy model for high-volume rapid iteration

• Speed: Very fast (2-3 seconds)
• Resolution: Up to 1024px
• Best for: High-volume generation, quick drafts where 4K is not needed
• model_tier: "flash"

🤖 Automatic Selection (Recommended)

By default, the server uses AUTO mode which routes to NB2 unless Pro's deeper reasoning is clearly needed:

Pro Model Selected When:

• Strong quality keywords: "4K", "professional", "production", "high-res", "HD"
• High thinking level requested: thinking_level="HIGH"
• Multi-image conditioning with multiple input images