Which integrations are available for this server?

Provides AI-powered tools for image generation and text-to-speech synthesis using OpenAI's API, supporting multiple voice options and image sizes.

How do I use Arcaelas MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Arcaelas MCP generate an image of a futuristic city at sunset with flying cars" 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.

@arcaelas/mcp

npm version License: MIT Node.js >= 18

MCP server providing AI-powered tools for audio generation, image generation, and image redesign using OpenAI-compatible APIs.

Build intelligent AI workflows with multimodal generation - Generate speech from text, create images from prompts, and redesign existing images using state-of-the-art AI models.

Features

🎤 Text-to-Speech with 8 natural voices
🎨 Image Generation from text prompts
🔄 Image Redesign with reference images
🚀 Dual Transport - stdio and HTTP/SSE
🔧 Type-Safe with Zod validation
⚡ Throttling to prevent API rate limits

Prerequisites

Node.js >= 18
OpenAI API key (or compatible endpoint)

Installation

Using npx (recommended)

Add to your ~/.config/claude/claude_desktop_config.json:

{ "mcpServers": { "arcaelas": { "command": "npx", "args": ["-y", "@arcaelas/mcp", "--stdio"], "env": { "OPENAI_API_KEY": "sk-..." } } } }

Global installation

npm install -g @arcaelas/mcp # Or with yarn yarn global add @arcaelas/mcp

Then in ~/.config/claude/claude_desktop_config.json:

{ "mcpServers": { "arcaelas": { "command": "arcaelas-mcp", "args": ["--stdio"], "env": { "OPENAI_API_KEY": "sk-..." } } } }

Environment Variables

Variable	Required	Default	Description
`OPENAI_API_KEY`	Yes	-	OpenAI API key for authentication
`OPENAI_BASE_URL`	No	`https://api.openai.com/v1`	Custom OpenAI-compatible API endpoint
`OPENAI_IMAGE_MODEL`	No	`dall-e-3`	Model to use for image generation and redesign
`OPENAI_AUDIO_MODEL`	No	`gpt-4o-mini-audio`	Model to use for audio generation

Available Tools

`audio(text, voice?)`

Generate speech audio from text using AI text-to-speech.

Parameters:

text (string, required): Text to convert to speech
voice (string, optional): Voice name - nova, alloy, echo, fable, onyx, shimmer, coral, sage (default: nova)

Returns: File path to the generated MP3 audio file.

Example:

await audio("Hello world, this is a test", "nova") // Returns: "/tmp/mcp-audio-xyz/audio.mp3"

`image(prompt, count?)`

Generate one or more images from a text prompt using AI.

Parameters:

prompt (string, required): Text description of the image(s) to generate
count (number, optional): Number of images to generate, 1-10 (default: 1)

Returns: Newline-separated file paths to the generated PNG images.

Example:

await image("A serene mountain landscape at sunset", 3) // Returns: "/tmp/mcp-image-abc/image_1.png\n/tmp/mcp-image-def/image_2.png\n/tmp/mcp-image-ghi/image_3.png"

Note: Each image generation includes a 700ms throttle delay to prevent API rate limiting.

`redesign(prompt, filename, count?)`

Redesign an existing image based on a text prompt.

Parameters:

prompt (string, required): Text description of how to redesign the image
filename (string, required): Absolute path to the source image file
count (number, optional): Number of redesigned images to generate, 1-10 (default: 1)

Returns: Newline-separated file paths to the generated PNG images.

Example:

await redesign("Make it look like a watercolor painting", "/path/to/photo.jpg", 2) // Returns: "/tmp/mcp-redesign-xyz/redesign_1.png\n/tmp/mcp-redesign-abc/redesign_2.png"

Note: Reads the source image, converts to base64, and uses vision API for redesign. Each generation includes a 700ms throttle delay.

CLI Arguments

Argument	Description
`--stdio`	Run in stdio mode (for Claude Desktop, etc.)
`--port <number>`	HTTP server port (default: 3100)
`--openai-key <key>`	OpenAI API key (overrides OPENAI_API_KEY env var)
`--openai-url <url>`	Custom OpenAI-compatible API endpoint (overrides OPENAI_BASE_URL)
`--image-model <model>`	Model for image generation (overrides OPENAI_IMAGE_MODEL)
`--audio-model <model>`	Model for audio generation (overrides OPENAI_AUDIO_MODEL)

Usage Examples

stdio Mode (Claude Desktop)

# Using environment variables OPENAI_API_KEY=sk-xxx npx -y @arcaelas/mcp --stdio # Using CLI arguments npx -y @arcaelas/mcp --stdio --openai-key sk-xxx

HTTP/SSE Mode (Cursor, etc.)

# Default port (3100) OPENAI_API_KEY=sk-xxx npx -y @arcaelas/mcp # Custom port with custom models npx -y @arcaelas/mcp --port 8080 \ --openai-key sk-xxx \ --image-model dall-e-3 \ --audio-model gpt-4o-mini-audio # With custom OpenAI-compatible endpoint npx -y @arcaelas/mcp --stdio \ --openai-url https://api.custom.ai/v1 \ --openai-key xxx

HTTP Endpoints (HTTP/SSE mode)

Endpoint	Method	Description
`/sse`	GET	Server-Sent Events connection
`/messages?sessionId=<uuid>`	POST	Send messages to specific session
`/health`	GET	Health check and server info

How It Works

All tools use OpenAI's /chat/completions endpoint with appropriate models and modalities:

audio: Uses the audio modality with the configured audio model (default: gpt-4o-mini-audio). Generates MP3 files with natural-sounding voices.
image: Generates images by sending prompts to the chat completions endpoint with the configured image model (default: dall-e-3). Supports batch generation with automatic throttling.
redesign: Similar to image but includes the source image as a base64-encoded image_url in the message content for vision-based redesign.

Generated files are stored in temporary directories (/tmp/mcp-*) and the file paths are returned to the client.

Development

# Clone repository git clone https://github.com/arcaelas/mcp.git cd mcp # Install dependencies npm install # Build npm run build # Run locally npm start # Watch mode npm run dev # MCP Inspector (for testing) npm run inspector

Architecture

This project uses modern MCP patterns inspired by best practices:

Zod Schemas for type-safe validation
McpServer API with registerTool() registration
Modular Structure with /lib/ for reusable code
Centralized Config in lib/config.ts
HTTP Client abstraction in lib/client.ts
Type Inference from Zod schemas

src/ ├── index.ts → Main entry point with tool registration ├── schemas.ts → Zod validation schemas ├── lib/ │ ├── config.ts → Centralized configuration │ └── client.ts → Configured HTTP client └── tools/ ├── audio.ts → Text-to-speech handler ├── image.ts → Image generation handler └── redesign.ts → Image redesign handler

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'feat: add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Security

See SECURITY.md for security policies and reporting vulnerabilities.

Changelog

See CHANGELOG.md for release history.

License

Support

📧 Email: arcaela.reyes@gmail.com
🐛 Issues: GitHub Issues
💬 Discussions: GitHub Discussions

Arcaelas MCP

@arcaelas/mcp

Features

Prerequisites

Installation

Using npx (recommended)

Global installation

Environment Variables

Available Tools

`audio(text, voice?)`

`image(prompt, count?)`

`redesign(prompt, filename, count?)`

CLI Arguments

Usage Examples

stdio Mode (Claude Desktop)

HTTP/SSE Mode (Cursor, etc.)

HTTP Endpoints (HTTP/SSE mode)

How It Works

Development

Architecture

Contributing

Security

Changelog

License

Links

Support

Resources

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