Image Generation MCP Server
# Image Generation MCP Server

[](https://smithery.ai/server/@GongRzhe/Image-Generation-MCP-Server)
This MCP server provides image generation capabilities using the Replicate Flux model.
## Installation
### Installing via Smithery
To install Image Generation MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@GongRzhe/Image-Generation-MCP-Server):
```bash
npx -y @smithery/cli install @GongRzhe/Image-Generation-MCP-Server --client claude
```
### Option 1: NPX Method (No Local Setup Required)
You can use the package directly from npm without installing it locally:
```bash
# No installation needed - npx will handle it
```
### Option 2: Local Installation
If you prefer a local installation:
```bash
# Global installation
npm install -g @gongrzhe/image-gen-server
# Or local installation
npm install @gongrzhe/image-gen-server
```
## Setup
### Configure Claude Desktop
Edit your Claude Desktop configuration file:
- On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- On Windows: `%APPDATA%/Claude/claude_desktop_config.json`
#### Option 1: NPX Configuration (Recommended)
This method runs the server directly from npm without needing local files:
```json
{
"mcpServers": {
"image-gen": {
"command": "npx",
"args": ["@gongrzhe/image-gen-server"],
"env": {
"REPLICATE_API_TOKEN": "your-replicate-api-token",
"MODEL": "alternative-model-name"
},
"disabled": false,
"autoApprove": []
}
}
}
```
#### Option 2: Local Installation Configuration
If you installed the package locally:
```json
{
"mcpServers": {
"image-gen": {
"command": "node",
"args": ["/path/to/image-gen-server/build/index.js"],
"env": {
"REPLICATE_API_TOKEN": "your-replicate-api-token",
"MODEL": "alternative-model-name"
},
"disabled": false,
"autoApprove": []
}
}
}
```
### Get Your Replicate API Token
1. Sign up/login at https://replicate.com
2. Go to https://replicate.com/account/api-tokens
3. Create a new API token
4. Copy the token and replace `your-replicate-api-token` in the MCP settings

### Environment Variables
- `REPLICATE_API_TOKEN` (required): Your Replicate API token for authentication
- `MODEL` (optional): The Replicate model to use for image generation. Defaults to "black-forest-labs/flux-schnell"
### Configuration Parameters
- `disabled`: Controls whether the server is enabled (`false`) or disabled (`true`)
- `autoApprove`: Array of tool names that can be executed without user confirmation. Empty array means all tool calls require confirmation.
## Available Tools
### generate_image
Generates images using the Flux model based on text prompts.


#### Parameters
- `prompt` (required): Text description of the image to generate
- `seed` (optional): Random seed for reproducible generation
- `aspect_ratio` (optional): Image aspect ratio (default: "1:1")
- `output_format` (optional): Output format - "webp", "jpg", or "png" (default: "webp")
- `num_outputs` (optional): Number of images to generate (1-4, default: 1)
#### Example Usage
```typescript
const result = await use_mcp_tool({
server_name: "image-gen",
tool_name: "generate_image",
arguments: {
prompt: "A beautiful sunset over mountains",
aspect_ratio: "16:9",
output_format: "png",
num_outputs: 1
}
});
```
The tool returns an array of URLs to the generated images.
## 📜 License
This project is licensed under the MIT License.
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose of generating images using the Flux model.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to cause inconsistency.
A single tool for an image generation server is too few for the apparent scope, as it lacks basic operations like listing models, managing generations, or handling variations. This minimal set will likely cause agent failures due to incomplete functionality.
The tool surface is severely incomplete for an image generation domain. It only provides generation without any supporting operations such as model selection, parameter tuning, history retrieval, or image editing, leading to dead ends in agent workflows.