Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments

Recraft MCP Server: The go-to scaling engine for AI models, leveraging the Model Context Protocol SDK to deliver seamless, context-aware deployments at enterprise scale.

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About Recraft MCP Server

What is Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments?

Recraft MCP Server is a specialized implementation of the Model Context Protocol (MCP) framework designed to integrate seamlessly with Recraft.ai's image generation services. This server enables developers to scale AI-driven image creation workflows while maintaining context-aware deployment strategies. By leveraging MCP’s standardized tool definitions, it provides a robust interface for enterprise applications needing to generate, transform, and optimize images at scale.

How to Use Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments?

Getting started involves a straightforward workflow: clone the repository, install dependencies, and configure environment variables. After setting up your Recraft API key, you can build and run the server in development or production modes. The MCP inspector tool simplifies testing, while shell scripts streamline deployment. Whether scaling for enterprise workloads or fine-tuning deployments for specific use cases, the server’s modular design ensures flexibility without sacrificing performance.

Recraft MCP Server Features

Key Features of Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments?

Built for reliability and scalability, this server offers type-safe schema validation via Zod, error-resistant API interactions, and support for over a dozen specialized image tools like background removal and creative upscaling. Its context-aware architecture allows dynamic resource allocation, making it ideal for high-throughput environments. Seamless integration with MCP-compliant LLM assistants further enhances its utility for end-to-end AI workflows.

Use Cases of Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments?

Recraft MCP Server FAQ

FAQ from Recraft MCP Server: Enterprise Scaling & Context-Aware Deployments?

Does the server support custom tool extensions? Yes, through MCP’s modular tool definitions, developers can add or modify capabilities without overhauling existing infrastructure.
What happens if the API key expires? The server returns a clear error code, prompting admins to update credentials via the .env file or environment variables.
How is performance monitored? Built-in logging and the Zod validation layer help track bottlenecks, while testing tools provide coverage insights for continuous improvement.
Can it run alongside legacy systems? Its Node.js foundation and MCP compatibility ensure smooth integration with existing tech stacks through RESTful endpoints.

Content

Recraft MCP Server

A Model Context Protocol (MCP) server implementation for integrating with Recraft.ai services. This server enables AI assistants to generate images through Recraft's API using the MCP framework.

🌟 Features

Implements MCP tools for Recraft image generation services
Provides type-safe schemas using Zod for validation
Supports various image generation options (styles, sizes, etc.)
Easy integration with LLM assistants that support MCP

📋 Prerequisites

Node.js (v18 or later recommended)
A Recraft API key from recraft.ai

🚀 Installation

Clone the repository:

git clone https://github.com/yourusername/recraft-mcp-server.git

cd recraft-mcp-server

Install dependencies:

npm install
Copy the environment file and configure your API key:

cp .env.example .env

Then edit .env and add your Recraft API key.

⚙️ Configuration

The server uses the following environment variables:

RECRAFT_API_URL: The Recraft API endpoint (default: https://external.api.recraft.ai)
RECRAFT_API_KEY: Your Recraft API key

🔧 Usage

Building the Server

npm run build

Starting the Server

npm start

Or use the provided shell script:

./start-mcp.sh

Development Mode

npm run dev

Inspecting the MCP Server

The MCP SDK includes an inspector tool to test the server:

npm run inspect

🧪 Testing

Run tests:

npm test

Run tests with coverage:

npm run test:coverage

Watch mode for tests:

npm run test:watch

🔍 Validation

The project uses Zod for schema validation. See ZOD_IMPLEMENTATION.md for details on the implementation.

📝 API

This server implements the following MCP tools:

generate_image: Generate images from text prompts
image_to_image: Transform an existing image based on a text prompt
inpaint_image: Edit parts of an image using a mask
replace_background: Replace the background of an image
vectorize_image: Convert raster images to vector format
remove_background: Remove the background from an image
crisp_upscale: Upscale images with enhanced detail and clarity
creative_upscale: Upscale images with creative enhancements
create_style: Create a new style based on reference images
get_user_info: Retrieve information about the current user
save_image_to_disk: Save generated images to the local filesystem

The server also responds to a special help command which provides general information about the available tools, but this is handled as a special case in the server logic rather than as a formal tool definition.

For detailed information about the available parameters and options for each tool, use the MCP inspector tool or review the tool definitions in the code.

🛠️ Development

Linting and Type Checking

# Run type checking
npm run type-check

# Run eslint
npm run lint

# Fix linting issues
npm run lint:fix

# Run both type checking and linting
npm run validate

📄 License

MIT

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