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sk-mcp-sample

by vtjc2002

This is a Semantic Kernel sample that demonstrates a server/client implementation using the MCP (Message Communication Protocol) over SSE (Server-Sent Events). It showcases how to integrate Semantic Kernel with a real-time communication protocol.

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What is sk-mcp-sample?

This sample demonstrates a Semantic Kernel application using a server/client architecture. It utilizes the Message Communication Protocol (MCP) for communication, specifically leveraging Server-Sent Events (SSE) as the transport mechanism.

How to use sk-mcp-sample?

Detailed instructions on how to use this sample are not provided in the README. However, it would likely involve setting up the server component, configuring the client to connect to the server, and then sending/receiving messages using the defined MCP protocol over SSE. Further documentation or code comments would be needed for specific usage details.

Key features of sk-mcp-sample

  • Semantic Kernel Integration

  • MCP Implementation

  • SSE Transport

  • Server/Client Architecture

  • Real-time Communication

Use cases of sk-mcp-sample

  • Real-time AI assistant

  • Chatbot with Semantic Kernel integration

  • Event-driven AI applications

  • AI-powered notification system

FAQ from sk-mcp-sample

What is Semantic Kernel?

Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, Hugging Face, and more with conventional programming languages like C#, Python, and JavaScript.

What is MCP?

MCP (Message Communication Protocol) is a protocol used for communication between the server and client. In this context, it likely defines the structure and format of messages exchanged.

What is SSE?

SSE (Server-Sent Events) is a server push technology enabling a server to automatically send data updates to a client's web browser. It's a one-way communication protocol where the server pushes data to the client.

What are the benefits of using SSE with Semantic Kernel?

SSE provides a real-time communication channel, allowing for immediate updates and responses from the Semantic Kernel-powered server to the client. This is beneficial for interactive AI applications.

Where can I find more documentation?

More documentation would likely be found within the code repository itself, including code comments, example usage scenarios, and potentially dedicated documentation files.