OpenRouter Image Generation MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OpenRouter Image Generation MCP Server generate an image of a sunset over the mountains"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OpenRouter Image Generation MCP Server
An MCP (Model Context Protocol) server that provides image generation capabilities through the OpenRouter API, supporting models like Gemini 2.5 Flash Image Preview.
Features
Image Generation: Generate images using Google Gemini 2.5 Flash Image Preview
Flexible Options:
Save generated images to local files
Related MCP server: OpenRouter MCP Multimodal Server
Installation
Clone the repository:
git clone https://github.com/yourusername/openrouter-image-gen-mcp.git
cd openrouter-image-gen-mcpInstall dependencies:
npm installBuild the TypeScript code:
npm run buildSet up your OpenRouter API key:
export OPENROUTER_API_KEY="your-api-key-here"You can get an API key from OpenRouter.
Configuration for Claude Desktop
Add the following to your Claude Desktop configuration file:
macOS/Linux
Location: ~/.config/claude/claude_desktop_config.json
Windows
Location: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"openrouter-image-gen": {
"command": "node",
"args": ["/path/to/openrouter-image-gen-mcp/dist/index.js"],
"env": {
"OPENROUTER_API_KEY": "your-api-key-here"
}
}
}
}Replace /path/to/openrouter-image-gen-mcp with the actual path to your installation directory.
Available Tools
1. generate_image
Generate images using AI models.
Parameters:
prompt(required): Text description of the image to generatemodel: Model to use (default:google/gemini-2.5-flash-image-preview:free)n: Number of images to generate (1-4, default: 1)size: Image dimensions (default:1024x1024)save_to_file: Save images locally (default: false)filename: Base filename for saved imagesshow_full_response: Include full base64 data in response (default: false, returns concise info only)
Example:
{
"prompt": "A serene Japanese garden with cherry blossoms",
"model": "google/gemini-2.5-flash-image-preview:free",
"save_to_file": true,
"filename": "japanese_garden"
}Note: Gemini image generation works through the chat completions API. The model will generate an image based on your prompt and return it as a URL or base64 data in the response. The size parameter is not used for Gemini models.
2. list_models
List all available image generation models.
Development
Build
npm run buildRun in development mode
npm run devStart the server
npm startAPI Documentation
Troubleshooting
401 Authentication Error
If you get a 401 error, check:
Your API key is correctly set in the environment or Claude Desktop config
The API key starts with
sk-or-(OpenRouter format)The API key is valid and has not expired
You have credits available in your OpenRouter account
Test your API key loading:
node test-api-key.jsCommon Issues
API Key not loading: Make sure the
OPENROUTER_API_KEYis set in your Claude Desktop config'senvsectionModel access denied: Some models require specific permissions or higher tier accounts
Image not generating for Gemini: Gemini uses the chat completions endpoint, not the images endpoint
License
WTFPL - Do What The Fuck You Want To Public License
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Available Tools
2 toolsgenerate_imageA
Generate images using Google Gemini API. Control image style, aspect ratio, and composition through descriptive text in your prompt.
Inputs: prompt (required), save_to_file, filename, show_full_response.
list_modelsB
Show information about the Gemini image generation model
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one generates images and the other lists available models. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (generate_image, list_models), making them predictable and easy to understand.
With only two tools, the server feels thin for an image generation service. While the core generation tool is present, the set may lack additional functionality like retrieving generation history or managing images, making it borderline.
The server covers the basic generate operation and model listing, but is missing potential features such as model selection parameter in generation, image deletion, or error handling details. This feels incomplete for a full image generation workflow.
Maintenance
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