A comprehensive Model Context Protocol (MCP) server that provides both **AI-powered image analysis** and **AI image generation** capabilities using
If you already use Ai Image MCP, the ai image mcp mcp server is the piece that lets your assistant work with it directly. 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.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Cursor — The Cursor tool exposed by this serverdescribe_image — Analyze an image and provide a detailed description. - Parameters: - image_path (str): Path to the image file - prompt (str, optional): Customanalyze_image_content — Perform targeted analysis of specific image aspects. - Parameters: - image_path (str): Path to the image file - analysis_type (str): Type ofcompare_images — Compare two images and highlight similarities and differences. - Parameters: - image1_path (str): Path to first image - image2_path (str): Pathget_image_metadata — Get technical metadata about an image file. - Returns: File size, dimensions, format, color mode, aspect ratio, etcgenerate_image — Generate images from text prompts using OpenAI's image generation models. - Parameters: - prompt (str): Text description of desired image - modeledit_image — Edit existing images using text prompts. - Parameters: - image_path (str): Path to image to edit - prompt (str): Description of desired edit -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)list_generated_images — List all generated images in a directory with metadata. - Parameters: - directory (str): Directory to scan (default: "./generated_images") -get_cache_info — Get information about the analysis cache (file count, size, location)clear_image_cache — The clear_image_cache tool exposed by this serverSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Configuration is passed through the environment: OPENAI_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
mcp[cli]>=1.9.4, openai>=1.90.0, pillow>=11.2.1, requests>=2.32.4Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Ai Image MCP's toolset — Cursor, describe_image, analyze_image_content and 8 more — is a fair guide to whether it matches your workflow. It is maintained by kareemaly; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Cursor | The Cursor tool exposed by this server. |
| 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**: Cach |
| 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 |
| 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. |
| 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) - |
| 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 |
| 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 - |
| 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 |
| get_cache_info | Get information about the analysis cache (file count, size, location). |
| clear_image_cache | The clear_image_cache tool exposed by this server. |
{
"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"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
mcp[cli]>=1.9.4, openai>=1.90.0, pillow>=11.2.1, requests>=2.32.4| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.