appkit-mcp-image 1.9.2

MCP Image Generation Server

Image Generation MCP Server

A FastMCP server for generating and editing images using OpenAI's gpt-image-1 and Azure FLUX.1-Kontext-pro models. This component is part of the AppKit platform and is designed to be integrated into the main application.

Features

• Text-to-Image Generation: Create images from natural language prompts using multiple AI models
• Image Editing & Inpainting: Edit existing images with text prompts and optional masks (gpt-image-1)
• Multiple Formats: Output as PNG, JPEG, or WEBP with customizable quality
• Prompt Enhancement: Auto-refine prompts via LLM for better results

Configuration

This component uses AppKit's configuration system (appkit_commons) and is configured via MCPImageGeneratorConfig. The settings are loaded from configuration/config.yaml or environment variables mapped by appkit_commons.

Settings

Setting	Description	Default
backend_server	URL of the backend server (for retrieving images)	http://localhost:8000
max_file_size_mb	Maximum allowed file size for input images	10
max_images_to_keep	Storage retention limit for generated images	50
generator	Active image generator backend (azure or google)	azure
azure_api_key	Azure OpenAI API key	None
azure_base_url	Azure OpenAI endpoint URL	None
azure_prompt_optimizer	LLM model used for prompt enhancement (Azure)	gpt-5-mini
azure_image_model	Image generation model identifier (Azure)	FLUX.1-Kontext-pro
google_api_key	Google AI API key	None
google_prompt_optimizer	LLM model used for prompt enhancement (Google)	gemini-2.0-flash-001
google_image_model	Image generation model identifier (Google)	imagen-4.0-generate-preview-06-06
auth_tokens	List of MCP tokens and scopes for authentication	[]

Integration

This module is designed to be integrated into the main AppKit application rather than running standalone. The create_image_mcp_server function returns a configured FastMCP instance which is mounted by the main application.

Usage in AppKit

In app/app.py, the server is initialized effectively as follows:

from appkit_commons.registry import service_registry
from appkit_user.authentication.services import get_verifier
from appkit_mcp_image.server import create_image_mcp_server, init_generators
from appkit_mcp_image.configuration import MCPImageGeneratorConfig

# ... inside initialization ...
image_mcp_config = service_registry().get(MCPImageGeneratorConfig)
_generators = init_generators(image_mcp_config)

# Create and mount the MCP server
servers["/image"] = create_image_mcp_server(
    _generators[image_mcp_config.generator],
    auth=get_verifier(),
)

Tools & API

generate_image

Create images from text descriptions.

Parameters:

Parameter	Type	Default	Description
prompt	string	required	Image description (max 32,000 chars)
size	string	1024x1024	Dimensions: 1024x1024, 1536x1024, 1024x1536, or auto
output_format	string	jpeg	Output format: png, jpeg, or webp
seed	integer	0	Random seed for reproducibility (0 = random)
enhance_prompt	boolean	true	Auto-enhance prompt via LLM
background	string	auto	Background: transparent, opaque, or auto

Example:

generate_image(
    prompt="A serene mountain landscape at sunset with golden light reflecting off a lake",
    size="1536x1024",
    output_format="png",
    enhance_prompt=True
)

edit_image

Edit existing images with text prompts and optional masks for inpainting.

Parameters:

Parameter	Type	Default	Description
prompt	string	required	Description of desired edits (max 32,000 chars)
image_paths	array	required	Image URLs, file paths, or base64 data URLs (max 16)
mask_path	string	optional	Optional mask image for inpainting (transparent areas indicate edit zones)
size	string	auto	Output dimensions
output_format	string	jpeg	Output format: png, jpeg, or webp
background	string	auto	Background setting

Example:

edit_image(
    prompt="Add a vibrant rainbow across the sky",
    image_paths=["https://example.com/landscape.jpg"],
    mask_path="https://example.com/sky_mask.png",
    output_format="png"
)

Image Input Formats

Supported input methods for image_paths parameter:

• HTTP/HTTPS URLs: https://example.com/image.jpg
• Local file paths: /path/to/image.png
• Base64 data URLs: data:image/png;base64,iVBORw0KG...

Inpainting with Masks

For precise control over edits, use mask images:

1. Create a PNG image with alpha transparency
2. Transparent areas (alpha=0) mark regions to edit
3. Opaque areas remain unchanged
4. Mask dimensions must match the input image

License

This project is licensed under the MIT License - see LICENSE.md for details.