What are the prerequisites to use this server?
Node.js v18 or later, npm or yarn, and a valid OpenAI API key with access to the selected image generation models.
@spartanz51
基本情報
MCP server for OpenAI Image Generation & Editing — text-to-image, image-to-image (with mask), no extra plugins.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"image-generator-gpt-image-15": {
"command": "npx imagegen-mcp --models gpt-image-1",
"env": {
"OPENAI_API_KEY": "YOUR_API_KEY"
}
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
This project provides a server implementation based on the Model Context Protocol (MCP) that acts as a wrapper around OpenAI's Image Generation and Editing APIs. It exposes image generation and editing capabilities as MCP tools, enabling integration with MCP-compatible clients. The server is intended for developers who want to use OpenAI's image models through the MCP standard.
The server can be run directly via npx imagegen-mcp [options]. It requires Node.js v18 or later and an OpenAI API key set in the OPENAI_API_KEY environment variable. After cloning the repository, install dependencies with npm install, build with npm run build, and run with node dist/index.js [options]. The server uses the StdioServerTransport to communicate via standard input/output.
Node.js v18 or later, npm or yarn, and a valid OpenAI API key with access to the selected image generation models.
The server uses the standard MCP `StdioServerTransport`, communicating via standard input and output streams.
The server supports DALL-E 2, DALL-E 3, and gpt-image-1 (if available/enabled) for text-to-image. Image-to-image editing supports DALL-E 2 and gpt-image-1.
DALL-E 3 only supports generating a single image per request (`n=1`). The `style` parameter (`vivid`/`natural`) is only applicable to DALL-E 3. Models are selected via the `--models` command-line argument, and the API key must have access to the chosen models.
Each generated image is saved to a temporary file, and the server returns the file path along with the base64 data of the image.

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