Azure OpenAI DALL-E 3 MCP Server

What does “Azure OpenAI DALL-E 3” MCP Server do?

The Azure OpenAI DALL-E 3 MCP Server is an integration layer that connects AI assistants and clients to Azure OpenAI’s DALL-E 3 image generation capabilities via the Model Context Protocol (MCP). By acting as a bridge between MCP-compatible clients and the Azure DALL-E 3 API, the server enables developers and AI workflows to programmatically generate images from natural language prompts, download created images, and facilitate advanced image-based tasks. This enhances development workflows by allowing easy access to powerful visual generation features directly from within AI-powered tools, automations, or interactive agents, supporting a wide range of creative, design, and content-generation use cases.

List of Prompts

No prompt templates are mentioned in the repository.

List of Resources

No resources are specified in the available documentation or code.

List of Tools

• generate_image
  Generates images using Azure OpenAI’s DALL-E 3 with configurable parameters such as prompt (required), size (image dimensions), quality (image quality), and style (image style).

• download_image
  Downloads generated images from a given URL to a specified local directory with a custom file name.

Use Cases of this MCP Server

• AI-powered Content Creation
  • Enable AI assistants to generate original images based on user descriptions for blog posts, articles, or presentations, streamlining the visual content design process.
• Automated Design Workflows
  • Integrate image generation into design pipelines, allowing for the rapid creation of mockups, concept art, or marketing materials by leveraging programmatic access to DALL-E 3.
• Prototyping and Ideation
  • Support creative brainstorming sessions where teams can visualize ideas instantly by converting text prompts into images during product development or pitch meetings.
• Educational and Illustrative Applications
  • Help educators or trainers generate custom illustrations or diagrams on the fly to enhance learning materials or interactive experiences.
• Data Augmentation for ML Pipelines
  • Use synthesized images for augmenting datasets for machine learning models, especially in scenarios lacking diverse visual data.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Clone or download the Azure OpenAI DALL-E 3 MCP Server repository.
3. Build the server:
   • Run npm install
   • Then run npm run build
4. Edit your Windsurf configuration to add the MCP server:

   {
     "mcpServers": {
       "dalle3": {
         "command": "node",
         "args": [
           "path/to/mcp-server-aoai-dalle3/build/index.js"
         ],
         "env": {
           "AZURE_OPENAI_ENDPOINT": "<endpoint>",
           "AZURE_OPENAI_API_KEY": "<key>",
           "AZURE_OPENAI_DEPLOYMENT_NAME": "<deployment>"
         }
       }
     }
   }
   

5. Save the config and restart Windsurf. Verify by invoking an MCP client request.

Claude

1. Install Node.js and clone the repo.
2. Build as above (npm install, npm run build).
3. Locate Claude’s MCP server configuration file.
4. Add the MCP server using the following JSON snippet:

   {
     "mcpServers": {
       "dalle3": {
         "command": "node",
         "args": [
           "path/to/mcp-server-aoai-dalle3/build/index.js"
         ],
         "env": {
           "AZURE_OPENAI_ENDPOINT": "<endpoint>",
           "AZURE_OPENAI_API_KEY": "<key>",
           "AZURE_OPENAI_DEPLOYMENT_NAME": "<deployment>"
         }
       }
     }
   }
   

5. Save changes, restart Claude, and test image generation.

Cursor

1. Confirm Node.js presence, clone and build the repo.
2. Edit Cursor’s configuration to add the MCP server:

   {
     "mcpServers": {
       "dalle3": {
         "command": "node",
         "args": [
           "path/to/mcp-server-aoai-dalle3/build/index.js"
         ],
         "env": {
           "AZURE_OPENAI_ENDPOINT": "<endpoint>",
           "AZURE_OPENAI_API_KEY": "<key>",
           "AZURE_OPENAI_DEPLOYMENT_NAME": "<deployment>"
         }
       }
     }
   }
   

3. Save and restart Cursor. Confirm setup by issuing a test request.

Cline

1. Install Node.js and dependencies, then build (npm install, npm run build).
2. Locate the Cline MCP config file and insert:

   {
     "mcpServers": {
       "dalle3": {
         "command": "node",
         "args": [
           "path/to/mcp-server-aoai-dalle3/build/index.js"
         ],
         "env": {
           "AZURE_OPENAI_ENDPOINT": "<endpoint>",
           "AZURE_OPENAI_API_KEY": "<key>",
           "AZURE_OPENAI_DEPLOYMENT_NAME": "<deployment>"
         }
       }
     }
   }
   

3. Save and restart Cline. Test the connection.

Securing API Keys

Use environment variables in the env section to securely store and reference your keys and endpoints. Example:

{
  "mcpServers": {
    "dalle3": {
      "command": "node",
      "args": [
        "path/to/mcp-server-aoai-dalle3/build/index.js"
      ],
      "env": {
        "AZURE_OPENAI_ENDPOINT": "${AZURE_OPENAI_ENDPOINT}",
        "AZURE_OPENAI_API_KEY": "${AZURE_OPENAI_API_KEY}",
        "AZURE_OPENAI_DEPLOYMENT_NAME": "${AZURE_OPENAI_DEPLOYMENT_NAME}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "dalle3": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "dalle3" to whatever the actual name of your MCP server is, and replace the URL with your own MCP server URL.

Overview

Based on the tables, the Azure OpenAI DALL-E 3 MCP Server covers the basics with clear tool support and security practices, but lacks prompt templates, resource definitions, and explicit roots/sampling support. The score reflects a functional but minimal MCP implementation.

MCP Score