MCP Server Runner: A Robust Bridge Between WebSocket Clients and MCP Servers

Overview: What is MCP Server Runner?

MCP Server Runner is a versatile WebSocket server implementation designed to facilitate interactions between WebSocket clients and Model Context Protocol (MCP) servers. It acts as an intermediary, enabling seamless communication and leveraging the MCP protocol suite to support a wide range of AI applications such as Claude Desktop, Continue, Cursor, and others.

🔧 Core Features & MCP Capabilities

The core capabilities of MCP Server Runner are centered around efficient WebSocket server management, bidirectional communication between clients and servers, process handling, graceful shutdown protocols, and comprehensive error logging. This application is built to ensure robust operation in both development and production environments, offering a solid foundation for developers building AI applications.

Environment Variables Configuration

Environment variables play a crucial role in configuring MCP Server Runner:

PROGRAM=        # Path to the MCP server executable (required if no config file)
ARGS=           # Comma-separated list of arguments for the MCP server
HOST=0.0.0.0    # Host address to bind to (default: 0.0.0.0)
PORT=8080       # Port to listen on (default: 8080)
CONFIG_FILE=    # Path to JSON configuration file

These variables allow for flexible and fine-grained control over the behavior of the WebSocket server and MCP servers it manages.

JSON Configuration File

Alternatively, you can provide a JSON configuration file that defines multiple server configurations and selects one as the default:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  },
  "defaultServer": "[server-name]",
  "host": "0.0.0.0",
  "port": 8080
}

Environment Variable Priority

The environment variables have a specific order of precedence in case they conflict:

1. Command-line specified config file
2. CONFIG_FILE environment variable
3. Environment variables (PROGRAM, ARGS, etc.)
4. Default values

⚙️ MCP Architecture & Protocol Implementation

MCP Server Runner adheres to the Model Context Protocol architecture, designed to enable scalable and flexible connections between WebSocket clients and AI application servers.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph TD
    A(Data) -[:processed]-> B[Data Processor]
    B --> C[Database]
    C -[:stored]-> D[Traffic Log]
    E[MCP Server] -->|Sends Requests To| J[Web Socket Client]
    F[RPC Client] -->|Initiates Requests To| G[MCP Server]
    style A fill:#e8f5e8
    style B fill:#f3e5f5
    style C fill:#f3e5f5
    style D fill:#d6eeef
    style E fill:#d9edf7
    style F fill:#e8f5e8

🚀 Getting Started with Installation

To get started with MCP Server Runner, follow these steps:

1. Install Rust 1.70 or Higher: Ensure that you have the latest version of Rust installed on your system.

2. Clone the Repository:

   git clone https://github.com/ModelContextProtocol/mcp-server-runner.git
   cd mcp-server-runner
   

3. Build and Run the Application with Environment Variables:

   export PROGRAM=npx
   export ARGS=-y,@modelcontextprotocol/server-github
   export PORT=8080
   cargo run
   

4. Using a Configuration File:

   • You can specify the configuration file as an argument or through the CONFIG_FILE environment variable.

   # Either specify the config file as an argument
   cargo run config.json
   
   # Or use the CONFIG_FILE environment variable
   CONFIG_FILE=config.json cargo run
   

5. Connect to the WebSocket Server:

   const ws = new WebSocket("ws://localhost:8080");
   

💡 Key Use Cases in AI Workflows

Real-Time Data Analysis Integration

Imagine a scenario where an AI application needs real-time data analysis from multiple sources. By integrating MCP Server Runner, the application can connect to various data providers and perform analyses on-the-fly through the established protocols.

Technical Implementation

import websocket
import json

url = "ws://localhost:8080"
ws = websocket.WebSocket()
ws.connect(url)

# Send a request for real-time data analysis
request_data = {"operation": "analysis", "data_source_ids": [1, 2]}
ws.send(json.dumps(request_data))

# Receive the results
response = ws.recv()

Model Training and Evaluation

For model training and evaluation tasks, MCP Server Runner acts as a central hub, enabling seamless interactions between different tools. It can manage multiple training processes across various datasets, ensuring efficient data flow and reduced dependency on manual interventions.

Technical Implementation

import subprocess
from threading import Thread

# Launching an MCP server instance for model training
def train_model(config):
    process = subprocess.Popen(["npx", "-y", "@modelcontextprotocol/server-training"], env={"CONFIG": config})
    try:
        # Perform some actions based on the output of the training process
        with open("training_output.txt", "r") as file:
            result = file.read()
        print(result)
    finally:
        process.terminate()

# Start a thread for the training process
Thread(target=train_model, args=("config.json",)).start()

🔌 Integration with MCP Clients

MCP Server Runner is designed to work seamlessly with various clients. The following table outlines compatibility and features:

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

📊 Performance & Compatibility Matrix

MCP Server Runner has been tested on multiple platforms and configurations, ensuring stability across various environments.

Cross-Platform Support

• Unix/Linux: Fully supported
• Windows: Requires minor adjustments in configuration due to different environment handling

🛠️ Advanced Configuration & Security

Setting up Authentication Mechanisms

To enhance security, you can implement authentication mechanisms using environment variables or custom configurations. For example:

AUTH_USER=youruser
AUTH_PASSWORD=yourpassword

Logging and Error Handling

MCP Server Runner includes comprehensive logging features that ensure detailed logs for debugging purposes. Any errors encountered during the runtime are logged and managed gracefully.

❓ Frequently Asked Questions (FAQ)

1. How do I integrate MCP Server Runner with my AI application?

Integrate by configuring the server to communicate with your chosen dataset or tool using the specified protocol.

2. What clients are compatible with MCP Server Runner?

The supported clients include Claude Desktop, Continue, and Cursor for full support; other clients may have limited features.

3. Can I use this for real-time data analysis?

Yes, MCP Server Runner supports real-time data streams and can be used to integrate such functionality seamlessly into your application.

4. How does it handle multiple client connections?

Currently, it supports one client connection at a time due to limitations in current implementation.

5. Is there SSL/TLS support for secure WebSocket connections?

No built-in SSL/TLS support; use a reverse proxy like Nginx or Traefik for securing the connection.

👨‍💻 Development & Contribution Guidelines

1. Fork the Repository: Start by forking the repository on GitHub.
2. Create Your Feature Branch:

   git checkout -b feature/amazing-feature
   

3. Commit Your Changes:

   git commit -m 'Add amazing feature'
   

4. Push to the Branch:

   git push origin feature/amazing-feature
   

5. Open a Pull Request: Submit your changes for review.

🌐 MCP Ecosystem & Resources

Explore additional resources that can help you understand and leverage the Model Context Protocol (MCP):

• Model Context Protocol Specification
• WebSocket Protocol (RFC 6455)

Conclusion

With its comprehensive features, MCP Server Runner is an indispensable tool for developers looking to integrate Model Context Protocol into their AI applications. By providing a robust and flexible bridge between WebSocket clients and MCP servers, it enables seamless data exchange and process management.

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This technical documentation aims to provide developers with the necessary information to effectively implement and utilize MCP Server Runner in their projects.