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PierrunoYT

Replicate Ideogram V3 MCP Server

by PierrunoYT

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

No local installation required! Use npx to run the server directly:

npx -y https://github.com/PierrunoYT/replicate-ideogram-v3-mcp-server.git

Option 2: Local Installation

  1. Clone this repository:

git clone https://github.com/PierrunoYT/replicate-ideogram-v3-mcp-server.git
cd replicate-ideogram-v3-mcp-server
  1. Install dependencies:

npm install
  1. Build the project:

npm run build

Configuration

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

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 generation

  • aspect_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 inpainting

  • style_type (optional): Style aesthetic (default: "None")

    • Options: "None", "Auto", "General", "Realistic", "Design"

  • style_reference_images (optional): Array of style reference image URLs

  • seed (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 ratios

  • 1:3, 3:1 - Ultra tall/wide ratios

  • 2:3, 3:2 - Classic photo ratios

  • 3:4, 4:3 - Standard ratios

  • 4:5, 5:4 - Social media ratios

  • 9:16, 16:9 - Widescreen ratios

  • 10:16, 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 HD

  • 1536x640 - Ultra-wide

  • 768x1344 - Portrait

  • 1344x768 - 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 prompt

  • On - Always applies magic prompt optimization

  • Off - 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 applied

  • Auto - Automatically selects appropriate style

  • General - General purpose style

  • Realistic - Photorealistic style

  • Design - 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.png

Error 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 dev

Testing the Server

npm test

Building the Project

npm run build

Getting the Installation Path

npm run get-path

API 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

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests if applicable

  5. Submit a pull request

Support

For issues and questions:

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 tools
generate_imageC

Generate an image using Ideogram V3 Balanced model via Replicate API. Supports text-to-image, inpainting, and style transfer.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for image generation
aspect_ratioNoAspect ratio. Ignored if a resolution or inpainting image is given.1:1
resolutionNoResolution. Overrides aspect ratio. Ignored if an inpainting image is given.None
magic_prompt_optionNoMagic Prompt will interpret your prompt and optimize it to maximize variety and quality of the images generated.Auto
imageNoAn image file to use for inpainting. You must also use a mask.
maskNoA black and white image. Black pixels are inpainted, white pixels are preserved.
style_typeNoThe styles help define the specific aesthetic of the image you want to generate.None
style_reference_imagesNoA list of images to use as style references.
seedNoRandom seed. Set for reproducible generation
style_presetNoApply a predefined artistic style to the generated image (V3 models only).None

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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

ParametersJSON Schema
NameRequiredDescriptionDefault
prediction_idYesThe prediction ID to check status for

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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

B3.1/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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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