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MCP Imagenate

Generate images and short Gemini video clips through one MCP server with selectable provider models and local file output.

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Install MCP Imagenate

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mcp-imagenate": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-imagenate"
      ],
      "env": {
        "GEMINI_API_KEY": "YOUR_VALUE_HERE",
        "NANO_BANANA_API_KEY": "YOUR_VALUE_HERE",
        "OPENAI_API_KEY": "YOUR_VALUE_HERE",
        "GPT_IMAGE_API_KEY": "YOUR_VALUE_HERE",
        "BFL_API_KEY": "YOUR_VALUE_HERE",
        "REVE_API_KEY": "YOUR_VALUE_HERE",
        "REVE_API_TOKEN": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

MCP Imagenate connects AI agents to Google Gemini, OpenAI gpt-image, BFL FLUX, and Reve for image generation, plus Google Gemini Omni for short video clips. Its `generate_image` tool accepts prompts, model selection, resolution, aspect ratio, background mode, and optional input images; `generate_video` handles Gemini Omni clips. Use it when an agent needs to create or edit visual assets across several providers while saving results to local directories. Node.js 22+ and at least one provider API key are required.

Use cases

  • Generate marketing or product images from agent-written prompts
  • Create transparent PNG assets for slides and compositing
  • Edit or transform images using local reference files
  • Produce short narrated or text-bearing Gemini video clips
  • Compare visual results across multiple image providers

Key features

  • Multi-provider image generation
  • Gemini Omni video generation
  • Reference image inputs
  • Transparent OpenAI image output
  • Configurable resolution and aspect ratio
  • Sandboxed local file paths

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

5/5 checks healthy over the last 51d

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Scanned 14h ago via OSV.dev · mcp-imagenate (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars16Stargazers on the source repository.
npm downloads1.4kPackage downloads in the last 30 days.
Last commit4d agoMost recent push to the default branch.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Imagenate works

What MCP Imagenate does

MCP Imagenate exposes image generation through a single MCP interface while allowing the configured provider and model to vary by request. Supported image providers are Google Gemini, OpenAI gpt-image, BFL FLUX, and Reve. Google Gemini also provides a separate video-generation path through Gemini Omni, producing short clips with audio and on-screen text.

The available image model names include nano-banana-2, nano-banana-2.1, and nano-banana-pro for Gemini; gpt-image-2.5-flare, gpt-image-2.5-sunburst, and gpt-image-2 for OpenAI; flux-2-klein, flux-2-pro, and flux-2-max for BFL; and reve-image for Reve. The default image model is gpt-image-2.5-flare.

How it works

An MCP client starts the server as a Node.js process, and the server makes requests to the providers whose API keys are configured. Generated files are written to the selected output directory. The image tool can also read local input images for supported provider workflows, allowing prompts to include reference or editing inputs.

generate_image accepts a prompt, model, resolution, aspect ratio, generation mode, background setting, thinking mode, output directory, and optional input image paths. Supported aspect ratios range from square and portrait formats to 21:9; resolutions are 1K, 2K, and 4K. Google models can return an image with accompanying text when mode is image_and_text.

generate_video uses the Gemini API key and the gemini-omni-1.1-flash model for clips from 3 to 10 seconds. The image and video capabilities use the same Google credential when Gemini is selected.

Setup and configuration

Node.js 22 or newer is required. Run the package directly with npx mcp-imagenate, or install it globally with npm. Set at least one provider credential before starting the process:

  • Gemini: GEMINI_API_KEY or NANO_BANANA_API_KEY
  • OpenAI: OPENAI_API_KEY or GPT_IMAGE_API_KEY
  • BFL: BFL_API_KEY
  • Reve: REVE_API_KEY or REVE_API_TOKEN

The alternative key names take precedence as documented for Gemini and OpenAI; REVE_API_KEY takes precedence over REVE_API_TOKEN. NANO_BANANA_OUTPUT_DIR optionally defines a base directory for saved images and sandboxes input and output paths within it. This is useful when restricting file access in production.

For Claude Desktop, add an mcp-imagenate entry that runs npx with mcp-imagenate as its argument and place the required credentials in the entry's env object.

Tools and capabilities

  • Generate images with Gemini, OpenAI, BFL FLUX, or Reve models.
  • Generate 3–10 second Gemini Omni video clips with audio.
  • Choose image resolution, aspect ratio, and output directory.
  • Send local reference images to supported image providers.
  • Request transparent PNG output with OpenAI gpt-image models.
  • Use Reve for typography and layout-focused generations.

Limitations and notes

Transparent backgrounds are supported only by OpenAI gpt-image models. Selecting transparency with Gemini, BFL FLUX, or Reve is rejected before the provider request is sent, and adding the phrase to the prompt does not change that limitation.

Reve ignores the requested resolution and returns its own large output. Its prompts are limited to 4,000 characters, and it accepts no more than eight reference images. Reve output may be PNG, JPEG, or WebP, with the saved extension matching the returned file format. A Reve generation costs 150 credits, typically takes 40–80 seconds, and should be given at least a 120-second timeout when called through a proxy or job runner.

Read the full README View source on GitHub

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Frequently Asked Questions about MCP Imagenate

MCP Imagenate is an MCP server that connects AI agents to Google Gemini, OpenAI gpt-image, BFL FLUX, and Reve for image generation, with Google Gemini Omni video support. Its main tools are `generate_image` for configurable image creation and `generate_video` for short Gemini Omni clips.

Install it by running `npx mcp-imagenate`, or install it globally with `npm install -g mcp-imagenate`. Node.js 22 or newer is required, and at least one supported provider API key must be set before starting the server.

MCP Imagenate supports Gemini, OpenAI, BFL FLUX, and Reve credentials. Use `GEMINI_API_KEY` or `NANO_BANANA_API_KEY`, `OPENAI_API_KEY` or `GPT_IMAGE_API_KEY`, `BFL_API_KEY`, and `REVE_API_KEY` or `REVE_API_TOKEN`; only one provider key is required.

Yes, but only OpenAI gpt-image models support transparent backgrounds. Set the image background to `transparent`; requests using Gemini, BFL FLUX, or Reve are rejected instead of returning an opaque image.

Yes. Claude Desktop can start MCP Imagenate with an `mcpServers` configuration that runs `npx mcp-imagenate`; provider credentials and optional output-directory settings go in the configuration's `env` object.

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