Image Generator MCP Server
# image-generator MCP Server
An mcp server that generates images based on image prompts
This is a TypeScript-based MCP server that implements image generation using **OPENAI**'s `dall-e-3` image generation model.
## Features
### Tools
- `generate_image` - Generate an image for given prompt
- Takes `prompt` as a required parameter
- Takes `imageName` as a required parameter to save the generated image in a `generated-images` directory on your desktop
## Development
Install dependencies:
```bash
npm install
```
Build the server:
```bash
npm run build
```
For development with auto-rebuild:
```bash
npm run watch
```
## Installation
To use with Claude Desktop, add the server config:
On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`
```json
{
"mcpServers": {
"command": "image-generator",
"env": {
"OPENAI_API_KEY": "<your-openai-api-key>"
}
}
}
```
Make sure to replace `<your-openai-api-key>` with your actual **OPENAI** Api Key.
### Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as a package script:
```bash
npm run inspector
```
The Inspector will provide a URL to access debugging tools in your browser.
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is singular and clear, eliminating any risk of misselection.
Since there is only one tool, naming consistency is inherently perfect; there are no other tool names to compare it against, so no inconsistencies can arise. The tool name follows a clear verb_noun pattern (generate_image).
A single tool is too few for a server named 'Image Generator MCP Server', as it suggests a limited scope that may not cover related operations like image editing, listing, or deletion. This minimal set could hinder agent workflows that require more comprehensive image management.
The tool set is severely incomplete for an image generation domain; it only provides generation without any support for retrieval, modification, deletion, or other common image operations. This creates significant gaps that will likely cause agent failures in broader tasks.