AOAI Dalle3 MCP Server
Description
# AOAI Dalle3 MCP Server An Azure OpenAI DALL-E integration server implementing the Model Context Protocol (MCP). This server provides a bridge between Azure OpenAI's DALL-E 3 image generation capabilities and MCP-compatible clients. ## Available Tools ### `generate_image`…
About
# AOAI Dalle3 MCP Server An Azure OpenAI DALL-E integration server implementing the Model Context Protocol (MCP). This server provides a bridge between Azure OpenAI's DALL-E 3 image generation capabilities and MCP-compatible clients. ## Available Tools ### `generate_image` Generates images using DALL-E 3 with the…
Details
- Author
- jacwu
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- Generate DALL-E 3 images via MCP tool
- Customizable size: 1024x1024, 1792x1024, 1024x1792
- Quality selection: standard or hd
- Style options: vivid or natural
- Download generated images to local storage
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
AOAI Dalle3 MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, run npm install and npm run build. Set the required environment variables (AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY) and optional ones. Configure an MCP client with the node ./build/index.js command and those environment variables. Then invoke the generate_image and download_image tools.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"aoai dalle3 mcp server": {
"dalle3": {
"command": "node",
"args": [
"./build/index.js"
],
"env": {
"AZURE_OPENAI_ENDPOINT": "",
"AZURE_OPENAI_API_KEY": "",
"AZURE_OPENAI_DEPLOYMENT_NAME": ""
}
}
}
}
}
McpServers
{
"dalle3": {
"command": "node",
"args": [
"./build/index.js"
],
"env": {
"AZURE_OPENAI_ENDPOINT": "",
"AZURE_OPENAI_API_KEY": "",
"AZURE_OPENAI_DEPLOYMENT_NAME": ""
}
}
}
AOAI Dalle3 MCP Server
An Azure OpenAI DALL-E integration server implementing the Model Context Protocol (MCP). This server provides a bridge between Azure OpenAI's DALL-E 3 image generation capabilities and MCP-compatible clients.
Available Tools
generate_image
Generates images using DALL-E 3 with the following parameters:
- prompt (required): Text description of the image to generate
- - size (optional): Image dimensions (default: 1024x1024). Available options:
- 1024x1024
- 1792x1024
- 1024x1792
- quality (optional): Image quality (default: hd). Available options:
- standard
- hd
- style (optional): Image style (default: natural). Available options:
- vivid
- natural
download_image
Downloads generated images to local storage:
- imageUrl (required): URL of the image to download
- localPath (required): Local directory path for saving
- fileName (required): Name for the downloaded file
Environment Variables
The following environment variables must be set to configure the server:
- AZURE_OPENAI_ENDPOINT: The endpoint URL for your Azure OpenAI resource. You can find this in the Azure portal under your OpenAI resource's "Keys and Endpoint" section.
- AZURE_OPENAI_API_KEY: The API key for your Azure OpenAI resource. This is also available in the "Keys and Endpoint" section.
- AZURE_OPENAI_DEPLOYMENT_NAME (optional, default: "dalle3"): The name of the DALL-E 3 deployment in your Azure OpenAI resource.
- OPENAI_API_VERSION (optional, default: "2024-02-15-preview"): The API version to use. Ensure this matches the version supported by your Azure OpenAI resource.
Build
1. Install dependencies:
npm install
2. Build the server:
npm run build
MCP Client Configuration
{
"dalle3": {
"command": "node",
"args": [
"./build/index.js"
],
"env": {
"AZURE_OPENAI_ENDPOINT": "<endpoint>",
"AZURE_OPENAI_API_KEY": "<key>",
"AZURE_OPENAI_DEPLOYMENT_NAME": "<deployment>"
}
}
}
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