Model Context Protocol (MCP) Server: Say

Overview: What is Say MCP Server?

The Say MCP Server is a critical component designed to facilitate communication between AI applications and various data sources, tools, and services through the Model Context Protocol (MCP). This server leverages macOS's built-in say command to synthesize text-to-speech functionality, making it an indispensable tool for real-time audio feedback in AI workflows.

The Say MCP Server supports a wide range of AI applications such as Claude Desktop, Continue, and Cursor. By integrating with these clients, it enables seamless text-to-speech processing, enhancing user experience by providing auditory notifications or synthesized voice responses to various prompts and commands. This integration is particularly valuable for applications where real-time voice feedback can improve usability and interaction.

🔧 Core Features & MCP Capabilities

The Say MCP Server offers several core features that enhance the MCP capabilities, ensuring robust communication between AI applications and diverse tools. Key among these are:

1. Cross-Client Compatibility: The server is fully compatible with MCP clients like Claude Desktop, Continue, and Cursor. This compatibility ensures a seamless integration experience across different platforms.

2. Real-Time Text-to-Speech Synthesis: Leveraging macOS's say command, the Say MCP Server can convert text into spoken words in real-time. This feature is invaluable for applications that require immediate audio feedback, such as error notifications or progress updates.

3. Customizable Voice and Language Settings: Users can customize the voice and language settings to suit their preferences, ensuring that synthesized speech matches the desired auditory experience.

4. Enhanced Audio Feedback: The server supports enhanced audio feedback mechanisms, allowing AI applications to provide more detailed and nuanced vocal responses based on specific prompts or user interactions.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Say MCP Server is designed to adhere strictly to the Model Context Protocol (MCP), ensuring seamless communication between different components. The server implementation involves the following key aspects:

1. MCP Client Integration: The server communicates with MCP clients via standard REST APIs, allowing real-time updates and data exchanges.
2. Text-to-Speech Processing: Utilizing macOS's say command for text-to-speech synthesis, the server processes incoming text prompts and converts them into spoken audio outputs.
3. Environment Configuration: The server is configured using environment variables, ensuring flexibility in customization based on specific requirements.

The following Mermaid diagram illustrates the protocol flow between an MCP client, the Say MCP Server, and a data source:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This flow ensures that the Say MCP Server can effectively process and send text prompts to data sources, which are then converted into spoken audio by the server.

🚀 Getting Started with Installation

To get started with the Say MCP Server, follow these steps:

1. Install Node.js: Ensure you have Node.js version 14.0.0 or higher installed on your system.
2. Clone the Repository: Clone the repository from GitHub:

   git clone https://github.com/bmorphism/say-mcp-server.git
   

3. Navigate to the Project Directory:

   cd say-mcp-server
   

4. Install Dependencies: Install all necessary dependencies using npm:

   pnpm install
   

5. Run the Server: Start the server with the appropriate configuration:

   npx node index.js [optional-args]
   

💡 Key Use Cases in AI Workflows

Use Case 1: Real-Time Error Notifications

In an application like Claude Desktop, real-time error notifications are crucial for ensuring user awareness. The Say MCP Server can be configured to provide auditory feedback when errors occur:

const say = require('flusspuler'); // Mocked library for example

// Example code snippet
function handleNotification(message) {
  console.log(`Error: ${message}`);
  say.speak(`There was an error. Please check your input and try again.`);
}

Use Case 2: Progress Updates During Long-Running Processes

For applications like Continue, providing users with real-time updates about the status of long-running processes can significantly enhance user engagement:

function processTask(task) {
  console.log(`Starting task ${task.id}...`);
  
  // Simulate a long-running task
  setTimeout(() => {
    say.speak(`Task ${task.id} has completed successfully.`);
  }, 5000);
}

🔌 Integration with MCP Clients

The Say MCP Server is designed to be highly compatible with various MCP clients, including:

• Claude Desktop: Full support for text-to-speech notifications.
• Continue: Real-time voice feedback mechanisms integrated.
• Cursor: Customizable audio responses tailored to user preferences.

📊 Performance & Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌ (Tools)	✅	❌ (Prompts)	Tools Only

🛠️ Advanced Configuration & Security

For advanced configuration, the Say MCP Server can be customized using environment variables. Here is an example of a configuration code snippet:

{
  "mcpServers": {
    "say": {
      "command": "node",
      "args": ["./index.js"],
      "env": {
        "API_KEY": "your-api-key",
        "VOICE": "Karen",
        "LANGUAGE": "en-US"
      }
    }
  }
}

These environment variables can be adjusted to fine-tune the server's behavior, ensuring optimal performance based on specific requirements.

❓ Frequently Asked Questions (FAQ)

1. How can I customize the voice and language settings?
   • You can set the VOICE and LANGUAGE environment variables to specify the desired voice and language for text-to-speech synthesis.
2. Can I use this server with other AI applications besides those listed in the compatibility matrix?
   • While the built-in support is for MCP clients like Claude Desktop, Continue, and Cursor, you can extend its compatibility by modifying the codebase to support additional clients.
3. How does the Say MCP Server handle errors or exceptions during text-to-speech processing?
   • The server logs any issues encountered during text-to-speech synthesis and provides fallback mechanisms in case of failures.
4. What security measures are in place for this server?
   • The server uses environment variables for secure configuration and can be further secured by implementing additional authentication mechanisms, if required.
5. Can I use this server to generate different types of audio content besides text-to-speech notifications?
   • While primarily designed for text-to-speech synthesis, the Say MCP Server can potentially be extended to handle other types of audio content through customization.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! If you wish to contribute to the development and improvement of this server:

1. Fork the Repository: Fork the repository on GitHub.
2. Install Dependencies: Ensure all dependencies are installed:

   pnpm install
   

3. Make Changes: Implement your features or fix bugs as needed.
4. Run Tests: Ensure that the tests pass before submitting a pull request.
5. Submit Pull Requests: Open a pull request to contribute your changes.

🌐 MCP Ecosystem & Resources

The Say MCP Server is part of a larger ecosystem that includes other MCP servers and tools designed for various AI applications. For more information, explore the repository documentation and community resources available online.

By integrating the Say MCP Server into your AI application, you can enhance the user experience with real-time audio feedback, making interactions more intuitive and engaging. Embrace this powerful model context protocol to unlock new possibilities in text-to-speech synthesis and auditory notifications!