Wait MCP Server

Overview

The Wait MCP Server is a lightweight, purpose‑built Model Context Protocol (MCP) service that introduces deliberate latency into an AI assistant’s workflow. By pausing for a configurable number of seconds before returning a response, the server enables developers to simulate real‑world network delays, test timeout handling, or orchestrate complex multi‑step interactions where timing matters. Its dual implementation in TypeScript and Python ensures compatibility across common development stacks while keeping the core logic identical.

Problem Solved

In many AI‑driven applications, developers need to validate how their systems behave under delayed responses—whether from external APIs, heavy computation, or intentional throttling. Traditional testing frameworks often lack a simple way to inject controlled pauses into the MCP dialogue flow, leading to brittle or incomplete tests. The Wait server fills this gap by providing a deterministic delay mechanism that can be tuned per request, allowing for reproducible latency scenarios without modifying the client or adding ad‑hoc mocks.

Core Functionality and Value

At its heart, the server listens for standard MCP requests and, upon receipt, holds execution for a user‑specified number of seconds before sending back the original payload or a generated acknowledgment. This pause is implemented in a non‑blocking fashion, so the server remains responsive to other incoming requests. For developers building AI assistants, this means they can:

• Simulate slow downstream services to verify graceful degradation or retry logic.
• Control pacing of conversational turns, useful for voice assistants that need to wait for user input or background processing.
• Test timeout handling in client libraries by configuring delays that exceed configured thresholds.

Because the server adheres strictly to MCP’s resource, tool, and prompt conventions, it can be dropped into any existing MCP‑based workflow with minimal friction.

Key Features

• Configurable delay: Accepts a parameter in each request, allowing per‑call customization.
• Language agnostic: Two separate codebases (TypeScript and Python) share the same API surface, giving teams flexibility based on their stack.
• Non‑blocking execution: Utilizes asynchronous primitives so the server can handle multiple concurrent wait requests without blocking.
• Minimal footprint: No external dependencies beyond the MCP SDK, keeping deployment lightweight.

Use Cases

• Integration testing: Inject controlled latency into end‑to‑end tests to ensure the assistant’s UI and error handling remain robust.
• Educational demos: Illustrate how AI assistants cope with delayed responses in a classroom or workshop setting.
• Load balancing simulation: Combine the Wait server with other MCP services to emulate a distributed system where different components have varying response times.
• Rate limiting experiments: Pair with an MCP rate‑limit checker to observe how the assistant behaves when consecutive requests are throttled.

Integration with AI Workflows

Developers can add the Wait server as an intermediate step in a chain of MCP calls. For example, after invoking a data‑retrieval tool, the assistant can route the result through the Wait server before passing it to a summarization tool. This pattern allows fine‑grained control over pacing without altering the underlying logic of each component. Because MCP clients already support chaining and conditional routing, inserting a wait step is as simple as adding another resource URL to the request pipeline.

Unique Advantages

The Wait MCP Server stands out by offering a purely protocol‑level solution to latency simulation, avoiding the need for external load generators or network proxies. Its dual implementation guarantees that teams working in TypeScript or Python can adopt the same testing strategy without code duplication. Moreover, by exposing delay as a request parameter rather than a hard‑coded setting, it provides unparalleled flexibility for dynamic scenarios where the required pause length may vary per interaction.