MCP Web Worker Demo

Overview

The MCP Web Worker Demo is a lightweight, browser‑based showcase that demonstrates how an AI assistant can interact with an MCP server using a Shared Web Worker. By moving the heavy lifting of the Model Context Protocol into a background thread, the demo solves the classic problem of keeping a web UI responsive while performing complex protocol operations such as tool invocation, resource retrieval, and prompt management. Developers who need to embed AI capabilities directly into web applications can use this pattern to offload protocol logic, reduce UI blocking, and maintain a clean separation between presentation and data handling.

At its core, the server provides a complete MCP interface: it exposes tools that can be called from the client, resources that can be listed and read, and prompts that can be fetched on demand. The worker implementation acts as a thin MCP client, translating between the browser’s messaging system and the raw MCP protocol. The main thread communicates with this worker through a simple, well‑defined API that handles connection setup, method calls, and result handling. This decoupling means the UI can evolve independently of the protocol logic, allowing developers to swap out or upgrade the MCP implementation without touching the front‑end code.

Key features of this setup include:

• Shared Worker Architecture – A single worker instance serves multiple tabs or windows, enabling shared state and reducing redundant network traffic.
• Responsive UI – Offloading MCP processing keeps the main thread free for rendering and user interaction, which is critical in rich web applications.
• Modular Design – The worker encapsulates all MCP logic, while the main thread only deals with high‑level method calls and UI updates.
• Security Isolation – Running protocol code in a separate worker context limits the attack surface and protects sensitive data from UI scripts.
• Diagnostics & Logging – Built‑in connection status indicators and detailed console logs help developers troubleshoot communication issues quickly.

Real‑world scenarios that benefit from this pattern include building AI‑powered web dashboards, collaborative tools where multiple users share the same toolset, or progressive web apps that need to cache and reuse MCP resources across sessions. By integrating the worker into an existing workflow, developers can expose AI functionalities to end users without compromising performance or maintainability.