Demo MCP Server

Overview

The Mcp Server Demo is a lightweight, full‑stack example that demonstrates how to expose an MCP (Model Context Protocol) server within a Blocklet application. By bundling both the server and client code into a single dapp, it provides developers with a ready‑made reference for integrating MCP capabilities—such as authentication, authorization, and real‑time communication—into their own AI assistant workflows.

Problem Solved

When building AI assistants that need to access external data or services, developers often face the challenge of securely exposing internal logic while maintaining a consistent protocol for tool invocation. Traditional approaches require custom API gateways or manual authentication plumbing, which can be error‑prone and hard to maintain. The Mcp Server Demo removes this friction by offering a pre‑configured MCP server that handles authentication and authorization out of the box, enabling seamless integration with Claude or other AI platforms that support MCP.

Core Functionality

• Authentication & Authorization: Leveraging the package, the server validates incoming requests against predefined credentials, ensuring that only trusted clients can invoke tools or resources.
• Server‑Side Rendering of MCP Endpoints: The demo sets up the endpoint for Server‑Sent Events (SSE), allowing real‑time streaming of model responses—a critical feature for interactive AI assistants.
• Full‑stack Packaging: By including both the API and client layers in a single Blocklet, developers can deploy the demo as a cohesive unit or extract individual components for their own projects.

Key Features Explained

• MCP Capability Flag: A simple setting () enables the MCP server within any Blocklet, making it trivial to toggle this feature on or off.
• Modular Server Code: The server logic resides in , while the SSE transport is isolated in . This separation promotes clean architecture and easy maintenance.
• Debugging Integration: The demo includes a link to the Blocklet dev store, allowing developers to test and debug their MCP server in a sandboxed environment before production deployment.

Use Cases & Scenarios

• AI‑powered Knowledge Bases: Connect an MCP server to a database or knowledge graph, letting Claude fetch answers via defined tools.
• Automated Workflows: Trigger external APIs (e.g., weather, finance) from within an AI conversation, with the server handling rate limiting and authentication.
• Real‑time Collaboration: Use SSE to stream live updates from the server back to the assistant, enabling features like dynamic content generation or streaming analytics.

Integration with AI Workflows

Developers can point their AI assistant’s MCP endpoint to the route exposed by this server. Once authenticated, the assistant can call any registered tool or resource defined on the server, receiving streaming responses that enhance user experience. The modular design means you can swap out the SSE transport for WebSockets or HTTP long‑polling if your deployment environment requires it.

Unique Advantages

• Zero Boilerplate: The demo removes the need to write repetitive authentication logic, allowing developers to focus on business logic.
• Blocklet Ecosystem Compatibility: By conforming to Blocklet conventions, the server can be easily integrated into larger dapp ecosystems or deployed on Blocklet’s hosting platform.
• Open‑Source Licensing: With an Apache 2.0 license, the code is freely reusable and modifiable, encouraging community contributions and rapid iteration.

In summary, the Mcp Server Demo serves as a practical, production‑ready blueprint for embedding MCP capabilities into AI assistants. It streamlines authentication, provides real‑time streaming support, and fits neatly into the Blocklet architecture—making it an invaluable starting point for developers looking to build secure, interactive AI applications.