mcp-imagenate

mcp-imagenate

An MCP server for image generation using multiple providers: Google Gemini, OpenAI (gpt-image), and BFL FLUX.

Providers & Models

Google Gemini (Nano Banana)

Name	Model ID	Best for
nano-banana-2	gemini-3.1-flash-image-preview	Fast, high-volume generation
nano-banana-pro	gemini-3-pro-image-preview	Highest quality output

OpenAI

Name	Model ID	Best for
gpt-image-2	gpt-image-2	Latest generation, improved detail

BFL FLUX

Name	Model ID	Best for
flux-2-klein	klein-4b	Fast, lightweight generation
flux-2-pro	pro-preview	Balanced quality and speed
flux-2-max	max	Maximum quality

Requirements

• Node.js 18+
• At least one provider API key

Installation

npx mcp-imagenate

Or install globally:

npm install -g mcp-imagenate

Setup

Set API keys for the providers you want to use:

# Google Gemini (at least one)
export GEMINI_API_KEY=your_key_here
# or
export NANO_BANANA_API_KEY=your_key_here

# OpenAI (at least one)
export OPENAI_API_KEY=your_key_here
# or
export GPT_IMAGE_API_KEY=your_key_here

# BFL FLUX
export BFL_API_KEY=your_key_here

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-imagenate": {
      "command": "npx",
      "args": ["mcp-imagenate"],
      "env": {
        "GEMINI_API_KEY": "your_key_here",
        "NANO_BANANA_OUTPUT_DIR": "/path/to/image/output"
      }
    }
  }
}

Environment Variables

Variable	Required	Description
GEMINI_API_KEY	*	Google AI Studio API key
NANO_BANANA_API_KEY	*	Alternative to GEMINI_API_KEY (takes precedence)
OPENAI_API_KEY	*	OpenAI API key
GPT_IMAGE_API_KEY	*	Alternative to OPENAI_API_KEY (takes precedence)
BFL_API_KEY	*	BFL FLUX API key
NANO_BANANA_OUTPUT_DIR	No	Base directory for saved images. When set, all output and input paths are sandboxed within this directory. Recommended for production.

* At least one provider API key must be set.

Tool: generate_image

Parameters

Parameter	Type	Default	Description
prompt	string (1-32,000 chars)	-	Text prompt describing the image
model	see Models above	"gpt-image-2"	Model to use (available models depend on configured API keys)
resolution	"1K" | "2K" | "4K"	"1K"	Output image resolution
aspectRatio	see below	"1:1"	Aspect ratio of the image
mode	"image" | "image_and_text"	"image"	Return image only, or image with description (Google models only)
thinking	"none" | "auto"	"auto"	Controls model thinking (Google models only)
outputDir	string	"."	Directory where images will be saved
inputImages	string[]	-	File paths of images to send alongside the prompt (Google models, and OpenAI gpt-image models via the images.edit endpoint)

Supported aspect ratios

1:1, 2:3, 3:2, 3:4, 4:3, 9:16, 16:9, 21:9

Response

Returns a JSON object:

{
  "model": "gemini-3.1-flash-image-preview",
  "savedFiles": ["/path/to/image-1.png"],
  "settings": {
    "resolution": "1K",
    "aspectRatio": "9:16",
    "mode": "image"
  },
  "description": "..."
}

description is only present when mode is "image_and_text".

Use as a library

Besides the standalone MCP server, this package can be embedded in another host — an app, or another MCP server that wants to expose image generation as its own tool.

import { createRegistry, generateImageToDisk } from "mcp-imagenate";

// Keys are passed in explicitly; nothing here reads process.env.
const registry = createRegistry({ openai: myOpenAIKey, google: myGoogleKey });

if (registry.models.length === 0) {
  throw new Error("No image provider is configured");
}

const outcome = await generateImageToDisk({
  registry,
  prompt: "a calico cat asleep on a warm keyboard",
  model: registry.defaultModel!,
  aspectRatio: "16:9",
  outputDir: "/somewhere/to/write",
  // outputBaseDir defaults to null, meaning no path sandboxing. Set it to a
  // directory to confine both output and input paths within that directory.
});

console.log(outcome.savedFiles);

The library entry point never reads process.env, writes to stdio, or exits the process. To read keys from the conventional environment variables anyway, use the keysFromEnv() helper. The standalone server is available at mcp-imagenate/server.

Export	Purpose
createRegistry(keys)	Build a registry of the models available for the given keys
keysFromEnv(env?)	Read provider keys from environment variables
generateImageToDisk(options)	Generate images and write them to disk
resolveOutputDir / resolveInputImagePath	Path sandboxing helpers (opt-in)

Security

• Path sandboxing: When NANO_BANANA_OUTPUT_DIR is set, both output and input image paths are sandboxed within this directory. Symlinks that resolve outside the sandbox are rejected. For library embedders this is opt-in via outputBaseDir, since the host usually controls which paths reach the call.
• Input validation: Input images are validated for format (PNG/JPEG/WEBP/GIF) and size (max 20 MB).
• API key validation: The server exits immediately if no API keys are configured. The library reports this as an empty registry instead, leaving the decision to the host.

License

MIT