About Leonardo AI MCP Server
Leonardo AI MCP Server connects MCP clients to Leonardo AI’s image-generation API. It supports multiple models, batch creation, local or URL output, and credit checks.
Key features
- 01Generate images with multiple models
- 02Choose from varied art styles
- 03Generate 1–4 images per request
- 04Save images locally or retrieve URLs
- 05Check remaining credits
Use cases
- 01Create illustrations in a selected style
- 02Generate several image variations at once
- 03Check credits before starting a generation
MCP support
It exposes Leonardo AI image generation and related functions to MCP clients, including model listing and credit checks.
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 READMELeonardo AI MCP Server
An MCP (Model Context Protocol) server that provides access to Leonardo AI's image generation API with 150 free daily credits.
Features
- Generate high-quality images using various AI models
- 150 free credits daily (refreshes at midnight)
- Support for multiple art styles and models
- Batch generation (1-4 images at once)
- Save images locally or get URLs
- Check remaining credits
Available Models
- Leonardo Diffusion XL: High-quality general purpose model
- Leonardo Vision XL: Photorealistic and artistic styles
- Leonardo Anime XL: Anime and manga style images
- DreamShaper v7: Fantasy and dreamlike images
- Realistic Vision v5: Highly realistic photographs
Installation
- Clone this repository:
git clone https://github.com/nikolausm/leonardo-ai-mcp-server.git
cd leonardo-ai-mcp-server
- Install dependencies:
npm install
-
Get your Leonardo AI API key:
- Sign up at https://leonardo.ai (free)
- Go to API settings
- Generate an API key
- Copy the key
-
Set up environment variable:
export LEONARDO_API_KEY="your_api_key_here"
Configuration in Claude Desktop
Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"leonardo-ai": {
"command": "node",
"args": ["/path/to/leonardo-ai-mcp-server/index.js"],
"env": {
"LEONARDO_API_KEY": "your_api_key_here"
}
}
}
}
Usage
Generate an Image
// Basic usage
generate_image({
prompt: "a majestic castle on a hilltop at sunset"
})
// Advanced usage
generate_image({
prompt: "cute cartoon guinea pig with big eyes",
model: "leonardo-anime-xl",
negative_prompt: "realistic, scary, dark",
num_images: 2,
width: 768,
height: 768,
guidance_scale: 10,
save_path: "/tmp/guinea_pig.png"
})
Check Remaining Credits
check_credits()
List Available Models
list_models()
Free Credits System
- 150 credits daily (resets at midnight UTC)
- Each image costs approximately 2 credits
- That's about 75 images per day for free!
- No credit card required
Tips for Better Results
-
Use specific models:
leonardo-anime-xlfor anime/cartoon stylerealistic-vision-v5for photorealistic imagesdreamshaper-v7for fantasy art
-
Optimize dimensions:
- Use multiples of 8 (512, 768, 1024)
- Square images often work best
-
Negative prompts are powerful:
- Use them to avoid unwanted elements
- Example: "blurry, low quality, distorted"
Examples
Children's Book Illustration
generate_image({
prompt: "friendly guinea pig reading a book, children's illustration, soft pastel colors",
model: "leonardo-diffusion-xl",
negative_prompt: "realistic, dark, scary",
width: 768,
height: 768
})
Educational Poster
generate_image({
prompt: "educational poster showing guinea pig care, clean design, infographic style",
model: "leonardo-vision-xl",
num_images: 3,
guidance_scale: 12
})
Error Handling
The server handles common errors:
- Invalid API key
- Insufficient credits
- Generation failures
- Timeout issues
License
MIT
Author
Michael Nikolaus
Links
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.