Azure-AI-Image-Editor-MCP

satomic/Azure-AI-Image-Editor-MCP

3.2

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This is an MCP server that supports Azure AI Foundry image generation and editing capabilities.

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Azure Image Editor MCP Server

| English

This is an MCP (Model Context Protocol) server that supports Azure AI Foundry image generation and editing capabilities.

Features

  1. Text-to-Image Generation - Generate high-quality images from text descriptions using Azure AI Foundry models
  2. Image Editing - Edit and modify existing images
  3. Configurable Models - Support for multiple Azure AI models via environment variables

Demo

Click 👇 to go to the demo on YouTube

Using GitHub Copilot & Azure AI Foundry with FLUX 1 Kontext Full Walkthrough for Image Generation Demo

Project Structure

azure-image-editor/
├── .venv/                        # Python virtual environment
├── src/
│   ├── azure_image_client.py     # Azure API client
│   ├── mcp_server.py             # STDIO MCP server
│   └── mcp_server_http.py        # HTTP/JSON-RPC MCP server
├── tests/                        # Test files
├── logs/                         # Server logs
├── tmp/                          # Temporary files
├── requirements.txt              # Python dependencies
├── .env                          # Environment configuration
├── .env.example                  # Environment configuration template
└── README.md                     # Project documentation

Prerequisites

⚠️ Important: Before using this MCP server, you must deploy the required model in your Azure AI Foundry environment.

Azure AI Foundry Model Deployment

  1. Access Azure AI Foundry: Go to Azure AI Foundry
  2. Deploy the model: Deploy flux.1-kontext-pro (or your preferred model) in your Azure AI Foundry workspace
  3. Get deployment details: Note down your:
    • Base URL (endpoint)
    • API key
    • Deployment name
    • Model name

Without proper model deployment, the MCP server will not function correctly.

Installation and Setup

  1. Clone and setup environment:
git clone https://github.com/satomic/Azure-AI-Image-Editor-MCP.git
cd azure-image-editor
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# or .venv\Scripts\activate  # Windows
pip install -r requirements.txt

Server Modes

This project supports two MCP server modes:

1. STDIO Mode (Default)

Communicates via standard input/output. Suitable for VSCode integration.

2. HTTP/JSON-RPC Mode

Communicates via HTTP with JSON-RPC 2.0 protocol. Suitable for web applications and remote access.

Configuration

Configure STDIO Mode (VSCode MCP)

Add the following to your VSCode MCP configuration:

{
  "servers": {
    "azure-image-editor": {
      "command": "/full/path/to/.venv/bin/python",
      "args": ["/full/path/to/azure-image-editor/src/mcp_server.py"],
      "env": {
        "AZURE_BASE_URL": "https://your-endpoint.services.ai.azure.com", // deployment endpoint
        "AZURE_API_KEY": "${input:azure-api-key}",
        "AZURE_DEPLOYMENT_NAME": "FLUX.1-Kontext-pro", // The name you gave your deployment
        "AZURE_MODEL": "flux.1-kontext-pro", // Default model
        "AZURE_API_VERSION": "2025-04-01-preview" // Default API version
      }
    }
  },
  "inputs": [
    {
      "id": "azure-api-key",
      "type": "promptString",
      "description": "Enter your Azure API Key",
      "password": "true"
    }
  ]
}

Important: Replace /full/path/to/ with the actual absolute path to this project directory.

Configure HTTP/JSON-RPC Mode

Option 1: Run directly with environment variables
# Activate virtual environment
source .venv/bin/activate  # Linux/Mac
# or .venv\Scripts\activate  # Windows

# Set environment variables
export AZURE_BASE_URL="https://your-endpoint.services.ai.azure.com"
export AZURE_API_KEY="your-api-key"
export AZURE_DEPLOYMENT_NAME="FLUX.1-Kontext-pro"
export AZURE_MODEL="flux.1-kontext-pro"
export AZURE_API_VERSION="2025-04-01-preview"

# Optional: Configure server host and port (defaults to 127.0.0.1:8000)
export MCP_SERVER_HOST="0.0.0.0"  # Listen on all interfaces
export MCP_SERVER_PORT="8000"      # Server port

# Start the HTTP server
python src/mcp_server_http.py
Option 2: Use .env file

Create a .env file in the project root:

AZURE_BASE_URL=https://your-endpoint.services.ai.azure.com
AZURE_API_KEY=your-api-key
AZURE_DEPLOYMENT_NAME=FLUX.1-Kontext-pro
AZURE_MODEL=flux.1-kontext-pro
AZURE_API_VERSION=2025-04-01-preview

# Optional server configuration
MCP_SERVER_HOST=127.0.0.1
MCP_SERVER_PORT=8000
DEFAULT_IMAGE_SIZE=1024x1024

Then start the server:

source .venv/bin/activate
python src/mcp_server_http.py
Server Endpoints

When the HTTP server is running, the following endpoints are available:

  • JSON-RPC Endpoint: http://127.0.0.1:8000/ - Main JSON-RPC 2.0 endpoint (POST)
  • Health Check: http://127.0.0.1:8000/health - Server health status (GET)
Connecting to HTTP Server

Important for HTTP Mode: When using HTTP mode, even if you provide an output_path parameter, the server will:

  1. Save the image to the specified path on the server
  2. Also return the base64-encoded image data to the client

This allows the MCP client to receive the image data and save it locally without needing additional file transfer.

Using VSCode MCP Client:

{
  "servers": {
    "azure-image-editor-http": {
      "type": "http",
      "url": "http://127.0.0.1:8000"
    }
  }
}

Using curl:

# List available tools
curl -X POST http://127.0.0.1:8000/ \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/list", "params": {}}'

# Call generate_image tool
curl -X POST http://127.0.0.1:8000/ \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/call",
    "params": {
      "name": "generate_image",
      "arguments": {
        "prompt": "A beautiful sunset over mountains",
        "size": "1024x1024",
        "output_path": "./images/sunset.png"
      }
    }
  }'

Available MCP Tools

1. generate_image

Generate images from text prompts

Parameters:

  • prompt (required): English text description for image generation
  • size (optional): Image size - "1024x1024", "1792x1024", "1024x1792", default: "1024x1024"
  • output_path (optional): Output file path, returns base64 encoded image if not provided

Example:

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A beautiful sunset over mountains",
    "size": "1024x1024",
    "output_path": "/path/to/output/image.png"
  }
}
2. edit_image

Edit existing images with intelligent dimension preservation

Parameters:

STDIO mode:

  • image_path (required): Path to the image file to edit
  • prompt (required): English text description of how to edit the image
  • size (optional): Output image size, uses original dimensions if not specified
  • output_path (optional): Output file path

HTTP mode:

  • image_data_base64 (required): Base64 encoded image data
    • Supports raw base64 format: iVBORw0KGgoAAAANS...
    • Supports Data URL format: data:image/png;base64,iVBORw0KGgoAAAANS...
  • prompt (required): English text description of how to edit the image
  • size (optional): Output image size, uses original dimensions if not specified
  • output_path (optional): Output file path (server-side), image data always returned to client

Example (STDIO mode):

{
  "name": "edit_image",
  "arguments": {
    "image_path": "/path/to/input/image.png",
    "prompt": "Make this black and white",
    "output_path": "/path/to/output/edited_image.png"
  }
}

Example (HTTP mode):

{
  "name": "edit_image",
  "arguments": {
    "image_data_base64": "iVBORw0KGgoAAAANS...",
    "prompt": "Make this black and white",
    "output_path": "/tmp/edited_image.png"
  }
}

Or using Data URL format:

{
  "name": "edit_image",
  "arguments": {
    "image_data_base64": "data:image/png;base64,iVBORw0KGgoAAAANS...",
    "prompt": "Make this black and white",
    "output_path": "/tmp/edited_image.png"
  }
}

Technical Specifications

  • Python version: 3.8+

  • Main dependencies:

    • mcp: MCP protocol support
    • httpx: HTTP client with timeout handling
    • pillow: Image processing and dimension detection
    • aiofiles: Async file operations
    • pydantic: Data validation
    • python-dotenv: Environment variable management
    • starlette: ASGI framework for HTTP server (HTTP mode only)
    • uvicorn: ASGI server (HTTP mode only)

  • Azure AI Foundry:

    • Default model: flux.1-kontext-pro (configurable)
    • Default API version: 2025-04-01-preview (configurable)
    • Supported image sizes: 1024x1024, 1792x1024, 1024x1792
    • Timeout: 5 minutes per request

Troubleshooting

  1. Timeout Errors: Image processing has 5-minute timeout, check network connectivity
  2. API Errors: Verify Azure credentials and endpoint URL
  3. Dependency Issues: Ensure virtual environment is activated and dependencies installed
  4. Server Connection Issues: Verify VSCode MCP configuration path is correct

License

MIT License