DALL-E MCP Server running on port
LocalAbout DALL-E MCP Server running on port
This MCP server lets AI clients generate, edit, and create variations of images through OpenAI's DALL-E API. It supports DALL-E 2 and DALL-E 3 generation, with editing and variations available for DALL-E 2.
Key features
- 01Generate images with DALL-E 2 or 3
- 02Edit images with DALL-E 2
- 03Create image variations with DALL-E 2
- 04Validate an OpenAI API key
- 05Choose image size and save location
Use cases
- 01Generate images from text prompts
- 02Edit an image using a prompt and optional mask
- 03Create alternate versions of an existing image
MCP support
The MCP server exposes image generation, editing, variation, and API-key validation tools to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEDALL-E MCP Server running on port
An MCP (Model Context Protocol) server for generating images using OpenAI's DALL-E API.
Features
- Generate images using DALL-E 2 or DALL-E 3
- Edit existing images (DALL-E 2 only)
- Create variations of existing images (DALL-E 2 only)
- Validate OpenAI API key
Installation
# Clone the repository
git clone https://github.com/oybekdevuz/dalle-mcp.git
cd dalle-mcp
# Install dependencies
npm install
# Build the project
npm run build
Usage
Running the Server
# Run the server
npm start
Important Note for Cline Users
When using this DALL-E MCP server with Cline, it's recommended to save generated images in your current workspace directory by setting the saveDir parameter to match your current working directory. This ensures Cline can properly locate and display the generated images in your conversation.
Example usage with Cline:
{
"prompt": "A tropical beach at sunset",
"saveDir": "/path/to/where-want-to/save/image"
}
Configuration for Cline
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse.js";
import { McpName } from "src/enum/McpName";
async function setupClient() {
let client: any = undefined;
const baseUrl: any = "http://127.0.0.1:3002/mcp";
try {
client = new Client({
name: 'streamable-http-client',
version: '1.0.0'
});
const transport = new StreamableHTTPClientTransport(new URL(baseUrl) as any);
await client.connect(transport);
console.log("Connected using Streamable HTTP transport");
} catch (error) {
console.log("Streamable HTTP connection failed, falling back to SSE transport");
client = new Client({
name: 'dalle-image-generator',
version: '1.0.0'
});
const sseTransport = new SSEClientTransport(baseUrl);
await client.connect(sseTransport);
console.log("Connected using SSE transport");
}
}
setupClient()
Make sure to:
- Replace
your-api-key-herewith your OpenAI API key
Available Tools
generate_image
Generate an image using DALL-E based on a text prompt.
{
"prompt": "A futuristic city with flying cars and neon lights",
"saveDir": "/path/to/save/directory",
}
Parameters:
prompt(required): Text description of the desired imagemodel(optional): DALL-E model to use ("dall-e-2" or "dall-e-3", default: "dall-e-3")size(optional): Size of the generated image (default: "1024x1024")- DALL-E 3: "1024x1024", "1792x1024", or "1024x1792"
- DALL-E 2: "256x256", or "1024x1024"
quality(optional): Quality of the generated image, DALL-E 3 only ("standard" or "hd", default: "standard")style(optional): Style of the generated image, DALL-E 3 only ("vivid" or "natural", default: "vivid")n(optional): Number of images to generate (1-10, default: 1)saveDir(optional): Directory to save the generated images (default: current directory or SAVE_DIR from .env). For Cline users: Setting this to your current workspace directory is recommended for proper image display.fileName(optional): Base filename for the generated images without extension (default: "dalle-{timestamp}")
edit_image
Edit an existing image using DALL-E based on a text prompt.
⚠️ Known Issue (March 18, 2025): The DALL-E 2 image edit API currently has a bug where it sometimes ignores the prompt and returns the original image without any edits, even when using proper RGBA format images and masks. This issue has been reported in the OpenAI community forum. If you experience this issue, try using the
create_variationtool instead, which seems to work more reliably.
{
"prompt": "Add a red hat",
"imagePath": "/path/to/image.png",
"mask": "/path/to/mask.png",
"model": "dall-e-2",
"size": "1024x1024",
"n": 1,
"saveDir": "/path/to/save/directory",
"fileName": "edited-image"
}
Parameters:
prompt(required): Text description of the desired editsimagePath(required): Path to the image to editmask(optional): Path to the mask image (white areas will be edited, black areas preserved)model(optional): DALL-E model to use (currently only "dall-e-2" supports editing, default: "dall-e-2")size(optional): Size of the generated image (default: "1024x1024")n(optional): Number of images to generate (1-10, default: 1)saveDir(optional): Directory to save the edited images (default: current directory or SAVE_DIR from .env). For Cline users: Setting this to your current workspace directory is recommended for proper image display.fileName(optional): Base filename for the edited images without extension (default: "dalle-edit-{timestamp}")
create_variation
Create variations of an existing image using DALL-E.
{
"imagePath": "/path/to/image.png",
"model": "dall-e-2",
"size": "1024x1024",
"n": 4,
"saveDir": "/path/to/save/directory",
"fileName": "image-variation"
}
Parameters:
imagePath(required): Path to the image to create variations ofmodel(optional): DALL-E model to use (currently only "dall-e-2" supports variations, default: "dall-e-2")size(optional): Size of the generated image (default: "1024x1024")n(optional): Number of variations to generate (1-10, default: 1)saveDir(optional): Directory to save the variation images (default: current directory or SAVE_DIR from .env). For Cline users: Setting this to your current workspace directory is recommended for proper image display.fileName(optional): Base filename for the variation images without extension (default: "dalle-variation-{timestamp}")
validate_key
Validate the OpenAI API key.
{}
No parameters required.
Development
Testing Configuration
Note: The following .env configuration is ONLY needed for running tests, not for normal operation.
If you're developing or running tests for this project, create a .env file in the root directory with your OpenAI API key:
# Required for TESTS ONLY: OpenAI API Key
OPENAI_API_KEY=your-api-key-here
# Optional: Default save directory for test images
# If not specified, images will be saved to the current directory
# SAVE_DIR=/path/to/save/directory
For normal operation with Cline, configure your API key in the MCP settings JSON as described in the "Adding to MCP Settings" section above.
You can get your API key from OpenAI's API Keys page.
License
Updated to http server mcp.
Original repository is https://github.com/Garoth/dalle-mcp
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.