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mimo-3 avatar

MCP Imagenate

mimo-3/mcp-imagenate
15STDIOregistry active
Summary

Connects Claude to three image generation APIs: Google Gemini (including the Nano Banana models), OpenAI's gpt-image, and BFL FLUX. Exposes a single generate_image tool that takes prompts, model selection, resolution (1K/2K/4K), aspect ratio, and optional input images for editing workflows. Google models support a mode flag to return generated images with descriptions, plus thinking controls. Handles path sandboxing when you set NANO_BANANA_OUTPUT_DIR, validating that input and output paths stay within bounds. Returns saved file paths and metadata. Reach for this when you want to give Claude multi-provider image generation without building separate integrations for each API.

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mcp-imagenate

mcp-imagenate

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

Providers & Models

Google Gemini (Nano Banana)

NameModel IDBest for
nano-banana-2gemini-3.1-flash-image-previewFast, high-volume generation
nano-banana-progemini-3-pro-image-previewHighest quality output

OpenAI

NameModel IDBest for
gpt-image-2gpt-image-2Latest generation, improved detail

BFL FLUX

NameModel IDBest for
flux-2-kleinklein-4bFast, lightweight generation
flux-2-propro-previewBalanced quality and speed
flux-2-maxmaxMaximum quality

Reve

NameVersionBest for
reve-imagelatestTypography and layout fidelity

This provider calls Reve's v2/image/create endpoint. latest is the only version alias v2 exposes, and it is what the response reports back, so there is no dated build to pin to. Do not confuse it with the v1 endpoints, which still serve the older reve-create@20250915 model.

Things worth knowing before sending Reve a prompt written for another provider:

  • resolution is ignored — Reve has no size parameter and returns its own large output. Exact dimensions vary between requests: 16:9 came back as both 5408x3072 and 5376x3072, and 3:4 as 3456x4800.
  • Prompts are capped at 4,000 characters, and this provider rejects longer ones before spending a request.
  • inputImages become v2 references. Reve accepts at most eight; a longer list is rejected before any of the files are read.
  • The saved file's extension follows the format Reve actually returned (PNG, JPEG or WebP), which is detected from the bytes rather than assumed.
  • A generation costs 150 credits (about $0.20) and typically takes 40-80 seconds. Give any proxy or job runner in front of it a timeout of at least 120 seconds.

Requirements

  • Node.js 20+
  • 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

# Reve (at least one)
export REVE_API_KEY=your_key_here
# or
export REVE_API_TOKEN=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

VariableRequiredDescription
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
REVE_API_KEY*Reve partner API token (from the API console at api.reve.com)
REVE_API_TOKEN*Alternative to REVE_API_KEY (REVE_API_KEY takes precedence)
NANO_BANANA_OUTPUT_DIRNoBase 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

ParameterTypeDefaultDescription
promptstring (1-32,000 chars)-Text prompt describing the image
modelsee Models above"gpt-image-2"Model to use (available models depend on configured API keys)
resolution"1K" | "2K" | "4K""1K"Output image resolution
aspectRatiosee 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)
outputDirstring"."Directory where images will be saved
inputImagesstring[]-File paths of images to send alongside the prompt (Google models, OpenAI gpt-image models via the images.edit endpoint, and Reve via v2 references)
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.

ExportPurpose
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 / resolveInputImagePathPath 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

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Categories
AI & LLM Tools
Registryactive
Packagemcp-imagenate
TransportSTDIO
UpdatedMar 22, 2026
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