MCP Wait Server: Enhancing AI Applications through Customizable Delays

Overview: What is MCP Wait Server?

The MCP Wait Server is a specialized server application designed to provide a wait tool for AI applications that support the Model Context Protocol (MCP). This protocol enables seamless integration of AI applications with various data sources and tools, much like how USB-C facilitates connectivity between devices. By using this wait server, AI developers can create delays in their workflows, ensuring that long-running operations complete before proceeding.

🔧 Core Features & MCP Capabilities

The core feature of the MCP Wait Server is its wait tool, which allows an AI application to pause execution for a specified number of seconds. This capability is crucial for scenarios where certain processes need time to complete before further actions can be taken. For instance, long-running scripts or downloads should not proceed immediately but wait until they have reached their completion point.

In terms of MCP capabilities, this server operates by receiving an MCP_CALL waiting command, which contains a duration parameter in seconds. The server then waits for the specified period and sends back a notification indicating that it is ready to continue. This mechanism ensures that AI applications can manage wait times more effectively without hardcoded delays, enhancing flexibility and adaptability.

⚙️ MCP Architecture & Protocol Implementation

The MCP architecture leverages TypeScript for its implementation, which translates into compiled JavaScript (either using npm start or a standalone build). The server is structured to handle complex interactions while maintaining simplicity. Upon startup, it listens for incoming MCP requests. These could be generated by an AI application that wishes to introduce a delay in the execution flow.

Protocol Flow Diagram

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 diagram illustrates the flow of communication between an AI application, the MCP protocol server, and a potential data source. Each component is essential for ensuring that wait commands are executed smoothly.

🚀 Getting Started with Installation

Installing the MCP Wait Server can be done in several ways: using npx, through npm globally, or by building from the repository's source code. These methods provide varying levels of setup complexity and flexibility.

Option 1: Use with npx (Recommended - No Installation Required)

This is the simplest approach for developers who want to quickly integrate the wait functionality into their AI applications without additional setup:

# Using npx
npx mcp-wait-server@latest

For integration:

{
  "mcpServers": {
    "wait_server": {
      "command": "npx",
      "args": ["mcp-wait-server@latest"],
      "env": {
        "MCP_WAIT_MAX_DURATION_SECONDS": "210",
        "MCP_WAIT_TOOL_DESCRIPTION": "Waits for a specified number of seconds. Use this to create a delay after starting a long-running operation (like a script or download via another tool), allowing it time to complete before you proceed or check its status."
      }
    }
  }
}

Option 2: Install from npm

For those who prefer a global installation:

# Install globally
npm install -g mcp-wait-server

Followed by configuring your AI application's MCP client appropriately.

Option 3: Clone and Build from Source

Designed for developers needing more control over the environment settings or making custom modifications to the server:

# Step-by-step installation process
git clone https://github.com/Amico1285/mcp-wait-server.git
cd mcp-wait-server

# Install dependencies
npm install

# Build the project
npm run build

# Run locally
npm start

💡 Key Use Cases in AI Workflows

The MCP Wait Server is particularly beneficial for several workflows. For example, when running scripts that require user interaction or completing complex tasks like file downloads:

Example 1: Script Execution

When a script detects that a critical operation needs time to complete (e.g., waiting for data processing), it can call the wait tool provided by this server.

import { MCPClient } from "@modelcontextprotocol/mcp";

const client = new MCPClient({ apiUrl: "http://localhost/wait_server" });

async function runScript() {
  // Start script execution
  await client.callTool("wait", { seconds: 120 });
}

runScript();

Example 2: Download Manager

In a download manager application, the server can be used to introduce delays between checking file status or starting new downloads based on existing load conditions.

// Pseudo-code for download manager implementation
while (await fileIsAvailable(fileId)) {
    // Use wait tool with custom duration if necessary
    await client.callTool("wait", { seconds: 30 }); 
}

These examples showcase how the MCP Wait Server can improve flow management and resource utilization in AI applications.

🔌 Integration with MCP Clients

The MCP Wait Server is fully compatible with several popular AI clients that support the Model Context Protocol. These include:

• Claude Desktop: ✅
• Continue: ✅
• Cursor: ❌ (Limited to tools only)

Ensure your configuration file accurately reflects this compatibility matrix.

📊 Performance & Compatibility Matrix

While the wait server offers broad compatibility, its performance depends on several factors. Detailed metrics include:

• Processor utilization during the waiting period is minimal.
• Network latency will affect how quickly responses are received.
• The maximum waiting time is configurable up to 210 seconds.

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

🛠️ Advanced Configuration & Security

Further customization of the wait server can be achieved via environment variables. Developers can adjust these parameters as needed:

{
  "mcpServers": {
    "wait_server": {
      "command": "npx",
      "args": ["mcp-wait-server@latest"],
      "env": {
        "MCP_WAIT_MAX_DURATION_SECONDS": "210",
        "MCP_WAIT_TOOL_DESCRIPTION": "Waits for a specified number of seconds. Use this to create a delay after starting a long-running operation (like a script or download via another tool), allowing it time to complete before you proceed or check its status."
      }
    }
  }
}

Security considerations also include setting up proper authorization and input validation to prevent unauthorized access.

❓ Frequently Asked Questions (FAQ)

1. Q: Can I use the MCP Wait Server with other AI clients besides those listed?

   • A: While primarily tested with Claude Desktop, Continue, and Cursor, any client that supports the Model Context Protocol should work seamlessly.

2. Q: How long can I set a single wait period for using this server?

   • A: The maximum duration for one wait call is 210 seconds, after which it will continue waiting if needed.

3. Q: Is the MCP Wait Server compatible with my current AI application infrastructure?

   • A: As long as your client supports the Model Context Protocol and follows the integration guidelines provided, compatibility should not be an issue.

4. Q: Can I modify the wait server to add more tools or functionalities?

   • A: Yes, the source code is open for customization. Visit the repository for detailed instructions.

5. Q: What are some best practices when integrating with MCP Wait Server?

   • A: Always ensure secure configuration storage and consider setting up automatic restarts in case of failure.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing to or improving this project:

1. Node.js 18+ is required.
2. Initialize the local environment using npm install.
3. Build the project with npm run build.

Contributions are welcome, and detailed guidelines can be found on the repository.

🌐 MCP Ecosystem & Resources

Explore more about the Model Context Protocol (MCP) in our official documentation:

• Model Context Protocol Overview
• MCP Client Integration Guide

To stay updated with latest developments, follow us on social media and join developer forums.

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This comprehensive MCP Wait Server documentation aims to provide a clear understanding of its value proposition within the broader context of AI application integration. It offers detailed insights into installation, use cases, configuration options, and more, helping developers leverage this tool effectively.