ws-mcp MCP Server: Enabling Seamless Integration of AI Applications

Overview: What is ws-mcp MCP Server?

ws-mcp is a WebSocket wrapper designed to integrate Model Context Protocol (MCP) servers with WebSockets, allowing real-time communication between various AI applications and backend services. It enables developers to connect AI tools like Claude Desktop, Continue, Cursor, and other MCP clients to specific data sources or tools using a standardized protocol.

🔧 Core Features & MCP Capabilities

ws-mcp offers essential capabilities that enhance the functionality of MCP servers:

1. WebSocket Integration: Wraps traditional MCP servers in WebSocket, facilitating bidirectional real-time communication.
2. Custom Configuration: Users can customize which MCP servers to run by modifying the configuration file.
3. Command Execution: Supports running commands for different MCP servers and provides flexible command-line options.
4. Environment Variable Support: Allows setting up environment variables for specific server configurations.

These features make ws-mcp a powerful tool in building versatile AI workflows with seamless integration across various platforms.

⚙️ MCP Architecture & Protocol Implementation

MCP architecture is designed to standardize the interaction between AI applications and backend services, using WebSocket as a key component. The protocol flow involves:

1. AI Application: Sends requests over WebSocket.
2. ws-mcp Server: Routes the requests to appropriate MCP servers based on configurations.
3. MCP Servers: Process the requests and return responses.
4. Data Source/Tool: Provides or receives data based on the processed requests.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph TD
    U[User Input] --> V[MCP Client]
    V --> W[MCP Protocol]
    W --> X[MCP Server]
    X --> Y[Data Source/Tool]
    Y --> Z[Processed Output]
    style U fill:#f5f8e1
    style V fill:#e5f3fe
    style W fill:#ffffff
    style X fill:#f5fafa
    style Y fill:#ffeecc
    style Z fill:#ffe6ff

🚀 Getting Started with Installation

To get ws-mcp running, follow these steps:

Prerequisites

1. Install Dependencies: Ensure you have uv installed.

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Create Configuration File: Copy the sample configuration file to your working directory and modify it as needed.

   cp sample.config.json config.json
   

Running ws-mcp

1. Basic Usage: Use default configurations with:

   uvx --refresh ws-mcp@latest
   

2. Custom Configuration: Use a custom configuration file and specific port:

   uvx --refresh ws-mcp@latest --config path/to/config.json --port 10125
   

💡 Key Use Cases in AI Workflows

ws-mcp allows developers to integrate various AI workflows, such as:

Example: Real-time Data Fetching

Suppose you need an endpoint where a user can fetch real-time data from an external API in the context of using Claude Desktop. You would use uvx mcp-server-fetch:

uvx --refresh ws-mcp --command "uvx mcp-server-fetch" --port 3002

This setup allows your AI application to query and receive real-time data seamlessly.

Example: System Operations & File Management

For system operations and file management, use wcgw server integrated into the workflow. On macOS:

uvx --refresh ws-mcp --command "uvx --from wcgw@latest --python 3.12 wcgw_mcp" --port 3001

This command ensures that system operations can be controlled via WebSocket connections.

🔌 Integration with MCP Clients

ws-mcp is compatible with various MCP clients, including but not limited to:

• Claude Desktop: Full support with real-time prompt processing.
• Continue: Supports full integration with data sources and tools.
• Cursor: Limited compatibility due to specific platform requirements.

Below is a compatibility matrix highlighting the status of each client:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

The performance and compatibility of ws-mcp are robust:

• Performance: Real-time data fetching operations show minimal lag, ensuring smooth user experience.
• Compatibility: Ensures backward and forward compatibility with various MCP servers and clients.

Here's a sample configuration file illustrating how to set up different server commands with environment variables:

{
  "mcpServers": {
    "fetch": {
      "command": "uvx",
      "args": ["mcp-server-fetch"],
      "env": {
        "API_KEY": "your-api-key"
      }
    },
    "wcgw": {
      "command": "uvx --from wcgw@latest --python 3.12 wcgw_mcp"
    }
  }
}

🛠️ Advanced Configuration & Security

Advanced configuration and security measures are crucial for maintaining a secure environment:

• Environment Variables: Securely manage API keys and other sensitive data.
• Firewall Rules: Implement firewall rules to restrict access.
• Log Monitoring: Regularly monitor logs to detect potential security breaches.

❓ Frequently Asked Questions (FAQ)

1. What is the difference between ws-mcp and typical MCP servers?

   • ws-mcp provides WebSocket integration, enabling real-time communication with MCP clients and servers.

2. Can I use ws-mcp with different AI applications?

   • Yes, it supports a variety of MCP clients such as Claude Desktop, Continue, Cursor, etc., ensuring broad compatibility.

3. How do I secure my MCP server setup?

   • Implement environment variable management for sensitive data and monitor logs regularly to enhance security.

4. Can ws-mcp handle complex real-time workflows?

   • Yes, it supports real-time data fetching and system operations, making it suitable for intricate AI workflows.

5. Is there a performance impact when using multiple servers with ws-mcp?

   • The architecture is designed to minimize latency and ensure smooth operation, even when handling multiple servers.

👨‍💻 Development & Contribution Guidelines

Contributing to the ws-mcp project involves the following steps:

1. Fork the Repository: Access the GitHub repository and fork it for your own development.
2. Set Up Development Environment: Install necessary dependencies.
3. Contribute Code: Make changes and submit pull requests.

🌐 MCP Ecosystem & Resources

Explore additional resources to leverage MCP further:

• MCP Documentation: Detailed guides on integration and usage.
• Community Forums: Engage with the community for support and collaboration.
• API Integrations: Extensive API documentation for seamless integration.

By utilizing ws-mcp, developers can streamline their AI application workflows while ensuring compatibility and real-time responsiveness.