AI Image MCP Server

A comprehensive Model Context Protocol (MCP) server that provides both AI-powered image analysis and AI image generation capabilities using OpenAI's Vision API and image generation models.

System Requirements

Tested on:

• macOS 14.3.0 (Darwin 23.3.0, ARM64)
• Python 3.13.0
• uv 0.7.13
• OpenAI API access

Features

🔍 Image Analysis & Description

• Smart Image Analysis: Analyze images using OpenAI's GPT-4O Vision model
• Targeted Analysis: Analyze specific aspects (objects, text, colors, composition, emotions)
• Image Comparisons: Compare two images and highlight similarities/differences
• Metadata Extraction: Get technical information about image files
• Intelligent Caching: Cache analysis results to avoid repeated API calls
• Multiple Formats: Support for PNG, JPEG, GIF, and WebP formats

🎨 Image Generation & Editing

• Text-to-Image Generation: Create images from text prompts using DALL-E 2, DALL-E 3, or GPT-Image-1
• Image Editing: Edit existing images with text prompts using GPT-Image-1 or DALL-E 2
• Image Variations: Create variations of existing images using DALL-E 2
• Flexible Output: Save generated images locally with custom naming and directories
• Model Support: Full support for all OpenAI image generation models with their specific features

MCP Tools

1. describe_image(image_path, prompt) - Get detailed image descriptions
2. analyze_image_content(image_path, analysis_type) - Analyze specific aspects
3. compare_images(image1_path, image2_path, comparison_focus) - Compare two images
4. get_image_metadata(image_path) - Extract technical metadata
5. get_cache_info() - View cache statistics
6. clear_image_cache() - Clear cached results

Installation

1. Install dependencies:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv add mcp[cli] openai pillow requests

2. Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key-here"

3. Run the server:

uv run main.py

Running the Server

uv run main.py

MCP Integration

Claude Desktop

{
  "mcpServers": {
    "ai-image-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/ai-image-mcp",
        "run",
        "main.py"
      ],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cursor

Configure MCP in Cursor settings:

{
  "servers": {
    "ai-image-mcp": {
      "command": "uv",
      "args": ["run", "main.py"],
      "cwd": "/absolute/path/to/ai-image-mcp",
      "env": {
        "OPENAI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Analysis Types

• general: Overall image description
• objects: Object detection and identification
• text: Text extraction and OCR
• colors: Color analysis and palette
• composition: Visual composition and layout
• emotions: Emotional content and mood

Project Structure

ai-image-mcp/
├── test_data/      # Sample images (gitignored)
├── tools/          # MCP tool definitions
├── utils/          # Utilities (caching, OpenAI client)
├── main.py         # Server entry point
└── server.py       # MCP server instance

Caching

• Automatic file change detection via SHA-256 hashes
• 30-day cache expiration
• Separate cache entries for different prompts/analysis types
• Significant performance improvements (1000x+ faster than API calls)

Available Tools

Image Analysis Tools

describe_image

Analyze an image and provide a detailed description.

• Parameters:
  • image_path (str): Path to the image file
  • prompt (str, optional): Custom analysis prompt
• Supports: PNG, JPEG, GIF, WebP
• Features: Caching, file validation, comprehensive error handling

analyze_image_content

Perform targeted analysis of specific image aspects.

• Parameters:
  • image_path (str): Path to the image file
  • analysis_type (str): Type of analysis - "general", "objects", "text", "colors", "composition", "emotions"
• Features: Specialized prompts for different analysis types

compare_images

Compare two images and highlight similarities and differences.

• Parameters:
  • image1_path (str): Path to first image
  • image2_path (str): Path to second image
  • comparison_focus (str): What to focus on in comparison

get_image_metadata

Get technical metadata about an image file.

• Returns: File size, dimensions, format, color mode, aspect ratio, etc.

Image Generation Tools

generate_image

Generate images from text prompts using OpenAI's image generation models.

• Parameters:
  • prompt (str): Text description of desired image
  • model (str): "dall-e-2", "dall-e-3", or "gpt-image-1" (default: dall-e-3)
  • size (str, optional): Image dimensions (varies by model)
  • quality (str, optional): Quality setting (varies by model)
  • style (str, optional): "vivid" or "natural" (DALL-E 3 only)
  • n (int, optional): Number of images (1-10, DALL-E 3 only supports 1)
  • output_dir (str): Directory to save images (default: "./generated_images")
  • filename_prefix (str): Prefix for filenames (default: "generated")

Model-Specific Features:

• DALL-E 2: Basic generation, sizes: 256x256, 512x512, 1024x1024
• DALL-E 3: High quality, styles (vivid/natural), sizes: 1024x1024, 1792x1024, 1024x1792
• GPT-Image-1: Advanced features, transparency support, compression control

edit_image

Edit existing images using text prompts.

• Parameters:
  • image_path (str): Path to image to edit
  • prompt (str): Description of desired edit
  • mask_path (str, optional): Path to mask image (PNG with transparent edit areas)
  • model (str): "gpt-image-1" or "dall-e-2" (default: gpt-image-1)
  • size, quality, n: Model-specific options
  • output_dir, filename_prefix: Output configuration

Supported Models: GPT-Image-1 (up to 16 images, 50MB each) and DALL-E 2 (1 square PNG, 4MB max)

create_image_variations

Create variations of existing images using DALL-E 2.

• Parameters:
  • image_path (str): Path to source image (must be square PNG, <4MB)
  • n (int): Number of variations (1-10, default: 2)
  • size (str): Variation size - "256x256", "512x512", "1024x1024"
  • output_dir, filename_prefix: Output configuration

list_generated_images

List all generated images in a directory with metadata.

• Parameters:
  • directory (str): Directory to scan (default: "./generated_images")
• Returns: File listing with sizes, dimensions, modification dates

Cache Management Tools

get_cache_info

Get information about the analysis cache (file count, size, location).

clear_image_cache

Clear all cached analysis results.

Model Comparison

Feature	DALL-E 2	DALL-E 3	GPT-Image-1
Generation	✅ Basic	✅ High Quality	✅ Advanced
Editing	✅ Limited	❌	✅ Advanced
Variations	✅	❌	❌
Max Images	10	1	10
Sizes	256x256, 512x512, 1024x1024	1024x1024, 1792x1024, 1024x1792	1024x1024, 1536x1024, 1024x1536
Styles	❌	vivid, natural	❌
Quality	standard	standard, hd	auto, high, medium, low
Transparency	❌	❌	✅
Max Prompt	1000 chars	4000 chars	32000 chars

Usage Examples

Generate a Basic Image

# Generate an image with DALL-E 3
generate_image(
    prompt="A serene mountain landscape at sunset with a crystal clear lake",
    model="dall-e-3",
    size="1792x1024",
    quality="hd",
    style="natural"
)

Edit an Existing Image

# Add elements to an image
edit_image(
    image_path="./photos/room.png",
    prompt="Add a beautiful bookshelf filled with colorful books to the left wall",
    model="gpt-image-1",
    quality="high"
)

Create Image Variations

# Create variations of a logo
create_image_variations(
    image_path="./logos/logo.png",
    n=5,
    size="1024x1024"
)

Analyze Generated Images

# Analyze a generated image
describe_image(
    image_path="./generated_images/generated_1234567890_1.png",
    prompt="Describe the artistic style and composition of this generated image"
)

File Organization

Generated images are automatically organized in separate directories:

• ./generated_images/ - Text-to-image generations
• ./edited_images/ - Image edits
• ./image_variations/ - Image variations

Files are named with timestamps to avoid conflicts:

• generated_1234567890_1.png
• edited_1234567890_1.png
• variation_1234567890_1.png

Error Handling

The server includes comprehensive error handling for:

• Invalid image formats and file paths
• Model-specific parameter validation
• File size and dimension limits
• API quota and rate limiting
• Network connectivity issues
• Malformed prompts and parameters

Cache System

The analysis tools use an intelligent caching system:

• File Change Detection: Uses SHA-256 hashes to detect file changes
• 30-Day Expiration: Automatically expires old cache entries
• Safe Operation: Cache failures don't affect main functionality
• Efficient Storage: Uses MD5 hashes for safe cache key generation

Requirements

• Python 3.13+
• OpenAI API key with access to Vision API and Image Generation
• Required packages: mcp[cli]>=1.9.4, openai>=1.90.0, pillow>=11.2.1, requests>=2.32.4

License

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