mcp-websocket-server: MCP Server for Real-time Data Updates

Overview: What is mcp-websocket-server?

mcp-websocket-server is an advanced implementation of the Model Context Protocol (MCP) server, designed to provide real-time data updates through WebSocket enhancements. This server supports traditional MCP requests while enabling clients—such as AI applications like Claude Desktop, Continue, Cursor—to subscribe for push notifications when new data becomes available. By leveraging MCP, this server acts as a bridge between AI applications and diverse data sources or tools, ensuring seamless integration and real-time collaboration.

🔧 Core Features & MCP Capabilities

mcp-websocket-server integrates the MCP protocol to facilitate direct communication between AI applications and external resources. The core features include:

1. MCP Server (Port 8080): Handles standard MCP requests from clients, enabling controlled, on-demand data access.
2. WebSocket Server (Port 8765): Establishes persistent connections for real-time updates, ensuring that subscribed clients receive notifications promptly when new data becomes available.
3. Push Notifications: Sends immediate updates to all connected clients upon the arrival of fresh data, enhancing efficiency and responsiveness.
4. Async Architecture: Utilizes asyncio to manage tasks asynchronously without blocking essential operations.

MCP Protocol Implementation

The server implements the MCP protocol by setting up clear communication channels between AI applications (MCP clients) and various backend services or data sources. This implementation ensures that requests are processed efficiently while maintaining a flexible, robust architecture capable of handling complex workflows.

⚙️ MCP Architecture & Protocol Implementation

mcp-websocket-server is architected to provide seamless integration with MCP clients through a well-defined protocol. The setup involves both an MCP server and a WebSocket server working in tandem:

1. MCP Client Compatibility Matrix:

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

2. MCP Protocol Flow Diagram:

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

This diagram illustrates the flow of data traffic between an AI application, the MCP client, the mcp-websocket-server (MCP server), and a data source or tool. The process involves the client initiating requests to the server, which in turn forwards them to relevant resources.

🚀 Getting Started with Installation

To get started with mcp-websocket-server, follow these steps for a smooth installation:

1. Clone this repository:

   git clone https://github.com/your-repo/mcp-websocket-server.git
   cd mcp-websocket-server
   

2. Install the required dependencies:

   pip install websockets mcp-sdk  # Replace with actual MCP SDK package name
   

3. Start the server:

   python server.py
   

💡 Key Use Cases in AI Workflows

In AI workflows, mcp-websocket-server offers significant benefits by ensuring real-time data availability and seamless integration across various tools and resources:

1. Real-Time Data Ingestion:

   • When developing data-driven AI models, real-time updates can help maintain model accuracy and relevance.

2. Tool Integration:

   • By connecting with external tools like database management systems or APIs for real-time data acquisition, mcp-websocket-server enables continuous data flow.

Example Use Case 1: Real-Time Data Ingestion

Imagine an AI application being used in a financial analysis project. The model needs to receive the latest market data hourly. Using mcp-websocket-server, the client can subscribe and receive updates from a real-time stock exchange API seamlessly integrated with the server. This dynamic update ensures that the AI model uses the most current data for analysis.

Example Use Case 2: Tool Integration

Consider an AI assistant that needs to pull data from multiple sources—like weather forecasts, news feeds, or social media trends—to provide contextual insights. mcp-websocket-server can facilitate these integrations by setting up persistent connections and ensuring real-time updates are pushed to the client.

🔌 Integration with MCP Clients

The server supports integration with various MCP clients such as Claude Desktop, Continue, and Cursor. Here’s how these clients connect:

1. WebSocket Subscription: Clients can subscribe for real-time updates by connecting to ws://localhost:8765 and sending a simple JSON message:

   {"action": "subscribe"}
   

2. MCP Request Handling: For traditional MCP requests, clients send HTTP GET/POST requests to the server at http://localhost:8080, with payloads like:

   {"method": "get_data"}
   

   The server processes these requests and sends back appropriate responses.

📊 Performance & Compatibility Matrix

mcp-websocket-server has been designed for high performance and compatibility, ensuring minimal latency and efficient data processing:

• Performance: Utilizes asyncio for non-blocking operations, allowing the server to handle multiple connections effectively.
• Compatibility: Fully compatible with MCP clients like Claude Desktop, Continue, and Cursor.

🛠️ Advanced Configuration & Security

For advanced usage, users can configure the server via a custom configuration file. Here’s an example of how to set up an MCP server:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Ensure that sensitive information, such as API keys and other credentials, are securely managed to prevent unauthorized access.

❓ Frequently Asked Questions (FAQ)

1. Q: Can this server handle large-scale deployment?

   • A: Yes, the asyncio architecture ensures efficient handling of multiple clients, making it suitable for large deployments.

2. Q: Is this server compatible with all MCP clients?

   • A: This version is fully compatible with Claude Desktop, Continue, and Cursor as shown in the compatibility matrix.

3. Q: How can I secure my MCP configuration?

   • A: Ensure that environment variables are securely managed and not stored in plain text within the configuration file.

4. Q: Are there any known issues with WebSocket subscriptions?

   • A: WebSocket subscriptions typically work seamlessly, but if clients experience disconnections, check network stability and server logs for common issues.

5. Q: How can I contribute to this project?

   • A: Pull requests are welcome! For major changes, please open an issue first to discuss the proposed implementation.

👨‍💻 Development & Contribution Guidelines

Contributions to mcp-websocket-server are highly encouraged! Follow these guidelines for submitting issues or pull requests:

1. Open an Issue: If you encounter any bugs or have feature suggestions, create a new issue on GitHub.
2. Fork the Repository: Clone your fork and make the necessary changes locally.
3. Run Tests: Ensure that all tests pass with pytest.
4. Commit Changes: Commit your changes following standard Git practices.
5. Create Pull Request: Submit a pull request to the main branch, including detailed descriptions of your changes.

🌐 MCP Ecosystem & Resources

mcp-websocket-server is part of an expanding ecosystem aimed at enhancing AI application development through MCP. Explore more resources and tools on the MCP Project website for additional integration opportunities.

By leveraging mcp-websocket-server, developers can build more robust and scalable AI applications that integrate seamlessly with external data sources and tools, driving innovation in the tech realm.