Replicate Ideogram V3 MCP Server
Provides access to the ideogram-ai/ideogram-v3-balanced image generation model via Replicate, enabling text-to-image generation, inpainting, style transfer with 60+ artistic presets, and support for 15 aspect ratios and 65+ custom resolutions.
Click on "Install 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., "@Replicate Ideogram V3 MCP Servergenerate a realistic sunset landscape in 16:9 with golden hour style"
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.
Replicate Ideogram V3 Balanced MCP Server
A Model Context Protocol (MCP) server that provides access to the ideogram-ai/ideogram-v3-balanced image generation model via Replicate. This server allows you to generate high-quality images using advanced AI technology through the Replicate platform.
Features
High-Quality Image Generation: Generate stunning images using the ideogram-ai/ideogram-v3-balanced model
Text-to-Image Generation: Create images from text prompts with advanced AI
Inpainting Support: Edit existing images using masks for targeted modifications
Style Transfer: Apply custom styles using reference images and 60+ artistic presets
Flexible Sizing Options: Support for 15 aspect ratios and 65+ custom resolutions
Advanced Controls: Seed-based reproducible generation, magic prompt optimization
Local Image Storage: Automatically downloads generated images to local storage
Prediction Tracking: Monitor generation status and retrieve results
Comprehensive Schema: Full support for all Ideogram V3 parameters
Installation
Option 1: Universal npx Installation (Recommended)
No local installation required! Use npx to run the server directly:
npx -y https://github.com/PierrunoYT/replicate-ideogram-v3-mcp-server.gitOption 2: Local Installation
Clone this repository:
git clone https://github.com/PierrunoYT/replicate-ideogram-v3-mcp-server.git
cd replicate-ideogram-v3-mcp-serverInstall dependencies:
npm installBuild the project:
npm run buildConfiguration
Environment Variables
Set your Replicate API token as an environment variable:
export REPLICATE_API_TOKEN="r8_NBY**********************************"You can get your API token from Replicate.
MCP Client Configuration
Universal npx Configuration (Recommended)
Add this server to your MCP client configuration:
{
"mcpServers": {
"replicate-ideogram-v3": {
"command": "npx",
"args": [
"-y",
"https://github.com/PierrunoYT/replicate-ideogram-v3-mcp-server.git"
],
"env": {
"REPLICATE_API_TOKEN": "r8_NBY**********************************"
}
}
}
}Local Installation Configuration
For local installations, use:
{
"mcpServers": {
"replicate-ideogram-v3": {
"command": "node",
"args": ["/path/to/replicate-ideogram-v3-mcp-server/build/index.js"],
"env": {
"REPLICATE_API_TOKEN": "r8_NBY**********************************"
}
}
}
}Available Tools
1. generate_image
Generate images using the Ideogram V3 Balanced model with comprehensive parameter support.
Parameters:
prompt(required): Text prompt for image generationaspect_ratio(optional): Aspect ratio (default: "1:1")Options: "1:1", "1:2", "2:1", "1:3", "3:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "10:16", "16:10"
resolution(optional): Specific resolution (overrides aspect_ratio, default: "None")65+ options from "512x1536" to "1536x640"
magic_prompt_option(optional): Magic prompt optimization (default: "Auto")Options: "Auto", "On", "Off"
image(optional): Image file for inpainting (requires mask)mask(optional): Black and white mask for inpaintingstyle_type(optional): Style aesthetic (default: "None")Options: "None", "Auto", "General", "Realistic", "Design"
style_reference_images(optional): Array of style reference image URLsseed(optional): Random seed for reproducible results (0-2147483647)style_preset(optional): Predefined artistic style (default: "None")60+ options including: "Art Deco", "Cubism", "Oil Painting", "Watercolor", "Pop Art", "Vintage Poster", etc.
Example:
{
"prompt": "A majestic mountain landscape at sunset with vibrant colors",
"aspect_ratio": "16:9",
"magic_prompt_option": "On",
"style_type": "Realistic",
"style_preset": "Golden Hour",
"seed": 12345
}2. get_image_status
Check the status of an image generation request using a prediction ID.
Parameters:
prediction_id(required): The prediction ID to check status for
Returns: Status information including completion state, errors, logs, and generated image URLs when complete.
Aspect Ratios and Resolutions
Supported Aspect Ratios
1:1- Square (default)1:2,2:1- Tall/wide ratios1:3,3:1- Ultra tall/wide ratios2:3,3:2- Classic photo ratios3:4,4:3- Standard ratios4:5,5:4- Social media ratios9:16,16:9- Widescreen ratios10:16,16:10- Extended ratios
Custom Resolutions
Choose from 65+ specific resolutions ranging from 512x1536 to 1536x640 pixels. The resolution parameter overrides the aspect_ratio setting when specified.
Popular Resolutions:
1024x1024- Square HD1536x640- Ultra-wide768x1344- Portrait1344x768- Landscape
Magic Prompt
The Magic Prompt feature interprets and optimizes your prompts to maximize variety and quality:
Auto(default) - Automatically decides when to use magic promptOn- Always applies magic prompt optimizationOff- Uses your prompt exactly as written
Magic Prompt also supports prompts in different languages and can enhance simple prompts with rich details.
Style Controls
Style Types
Control the overall aesthetic approach:
None(default) - No specific style appliedAuto- Automatically selects appropriate styleGeneral- General purpose styleRealistic- Photorealistic styleDesign- Design-focused style
Style Presets
Choose from 60+ predefined artistic styles:
Art Movements:
Art Deco, Art Brut, Bauhaus, Cubism, Pop Art
Photography Styles:
Golden Hour, Long Exposure, Dramatic Cinema, Editorial
Artistic Techniques:
Oil Painting, Watercolor, Woodblock Print, Halftone Print
Design Styles:
Flat Art, Minimal Illustration, Blueprint, Vintage Poster
And many more including: 80s Illustration, Anime, Graffiti, Surreal Collage, Travel Poster, etc.
Inpainting
Use the image and mask parameters for targeted image editing:
image: The base image to edit (URL or file path)mask: Black and white image where black pixels are inpainted and white pixels are preserved
Inpainting Example:
{
"prompt": "A beautiful garden with flowers",
"image": "https://example.com/base-image.jpg",
"mask": "https://example.com/mask.jpg"
}Style Reference Images
Provide an array of image URLs in style_reference_images to guide the style of generated images. The model will analyze these references and apply similar aesthetic qualities to your generation.
Style Reference Example:
{
"prompt": "A modern cityscape",
"style_reference_images": [
"https://example.com/style1.jpg",
"https://example.com/style2.jpg"
]
}Reproducible Generation
Use the seed parameter (0-2147483647) to generate reproducible results. The same prompt with the same seed will produce identical images.
Reproducible Example:
{
"prompt": "A red rose in a garden",
"seed": 42,
"aspect_ratio": "1:1"
}Output
Generated images are automatically downloaded to a local generated_images/ directory with timestamped filenames. The response includes:
Image URLs: Direct links to generated images
Local Paths: Local file locations for downloaded images
Generation Details: All parameters used in generation
Metadata: Timestamps, filenames, and technical details
Example Output:
Successfully generated image with Ideogram V3 Balanced model.
**Prompt:** A majestic mountain landscape at sunset
**Aspect Ratio:** 16:9
**Style Preset:** Golden Hour
**Seed:** 12345
**Generated Images:**
1. https://replicate.delivery/pbxt/abc123.png
**Local Copies:**
1. /path/to/generated_images/ideogram_2024-01-15T10-30-00-000Z_1.pngError Handling
The server provides detailed error messages for:
Missing or invalid API tokens
Invalid parameter values
Network connectivity issues
API rate limits and quotas
Generation failures and timeouts
File download errors
Development
Running in Development Mode
npm run devTesting the Server
npm testBuilding the Project
npm run buildGetting the Installation Path
npm run get-pathAPI Reference
This server implements the ideogram-ai/ideogram-v3-balanced API via Replicate. For detailed API documentation, visit:
Examples
Basic Text-to-Image
{
"prompt": "A cute cat wearing a wizard hat"
}High-Resolution Landscape
{
"prompt": "Epic mountain vista with dramatic clouds",
"resolution": "1536x640",
"style_preset": "Dramatic Cinema"
}Artistic Portrait
{
"prompt": "Portrait of a wise old man",
"aspect_ratio": "3:4",
"style_type": "Realistic",
"style_preset": "Oil Painting"
}Reproducible Design
{
"prompt": "Modern logo design for a tech company",
"aspect_ratio": "1:1",
"style_type": "Design",
"seed": 2024,
"magic_prompt_option": "Off"
}License
MIT License - see LICENSE file for details.
Contributing
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
Support
For issues and questions:
Open an issue on GitHub
Check the Replicate documentation
Review the MCP specification
Changelog
v1.0.0
Initial release with ideogram-ai/ideogram-v3-balanced integration
Comprehensive parameter support with full schema implementation
Text-to-image generation with 60+ style presets
Inpainting support with image and mask parameters
Style reference images for custom aesthetics
Magic prompt optimization with multilingual support
15 aspect ratios and 65+ custom resolutions
Seed-based reproducible generation
Local image download with organized storage
Robust error handling and status tracking
Available Tools
2 toolsgenerate_imageC
Generate an image using Ideogram V3 Balanced model via Replicate API. Supports text-to-image, inpainting, and style transfer.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text prompt for image generation | |
| aspect_ratio | No | Aspect ratio. Ignored if a resolution or inpainting image is given. | 1:1 |
| resolution | No | Resolution. Overrides aspect ratio. Ignored if an inpainting image is given. | None |
| magic_prompt_option | No | Magic Prompt will interpret your prompt and optimize it to maximize variety and quality of the images generated. | Auto |
| image | No | An image file to use for inpainting. You must also use a mask. | |
| mask | No | A black and white image. Black pixels are inpainted, white pixels are preserved. | |
| style_type | No | The styles help define the specific aesthetic of the image you want to generate. | None |
| style_reference_images | No | A list of images to use as style references. | |
| seed | No | Random seed. Set for reproducible generation | |
| style_preset | No | Apply a predefined artistic style to the generated image (V3 models only). | None |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions the model (Ideogram V3 Balanced) and API (Replicate) but doesn't disclose rate limits, authentication needs, cost implications, output format, generation time, or error handling. The three modes are listed but not explained operationally.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately brief (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first establishes the tool's function and context, the second enumerates capabilities. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 10-parameter image generation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns (image URL? binary data?), how to handle the output, error conditions, or important behavioral aspects like generation time or cost. The three modes are mentioned but not sufficiently contextualized.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing comprehensive parameter documentation. The description adds minimal value beyond the schema, only implying that 'prompt' is for text-to-image and that 'image' and 'mask' relate to inpainting. It doesn't explain parameter interactions or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates images using a specific model and API, and lists three supported modes (text-to-image, inpainting, style transfer). It distinguishes from the sibling 'get_image_status' by focusing on creation rather than status checking, though it doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions three use cases (text-to-image, inpainting, style transfer) but provides no guidance on when to choose one over another, prerequisites for inpainting (requires mask), or alternatives. It doesn't explain when this tool should be used versus other image generation tools that might exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_image_statusC
Check the status of an image generation request
| Name | Required | Description | Default |
|---|---|---|---|
| prediction_id | Yes | The prediction ID to check status for |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('check status') but doesn't describe what statuses are possible, whether it's idempotent, rate limits, authentication needs, or response format. This is inadequate for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what status information is returned (e.g., pending, completed, failed), potential errors, or how it integrates with the sibling tool. For a status-checking tool, this leaves significant gaps in understanding its behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the single parameter (prediction_id). The description adds no additional meaning beyond implying the parameter relates to an image generation request, which is already suggested by the tool name. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as checking the status of an image generation request, using specific verbs ('check') and resources ('image generation request'). It distinguishes from the sibling tool 'generate_image' by focusing on status checking rather than generation, though it doesn't explicitly mention the sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a prediction_id from generate_image), exclusions, or contextual cues for selection, leaving usage entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: generate_image initiates an image generation request, while get_image_status checks the status of an existing request. There is no overlap in functionality, making it easy for an agent to select the correct tool.
Both tools follow a consistent verb_noun pattern (generate_image, get_image_status) with clear, descriptive names. The naming is uniform and predictable, using snake_case throughout.
With only two tools, the server feels thin for its purpose of image generation via Ideogram V3. It lacks essential operations like retrieving generated images, managing requests (e.g., cancel), or handling variations, which limits functionality.
The toolset is severely incomplete for image generation workflows. While it covers initiating and checking status, there is no tool to retrieve the actual generated image, update requests, or handle errors, leaving agents unable to complete core tasks.
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