MCP Server with Semantic Kernel

Example Use Cases

• 1

  Integrate AI language models with external tools and memory using Semantic Kernel.

• 2

  That require standardized communication with tool servers via MCP using SSE or stdio.

• 3

  Build complex workflows combining AI understanding with actionable plugins.

Description

This repository demonstrates how to build Semantic Kernel agents using Model Context Protocol (MCP) plugins in Python. It enables AI agents to connect language models with tools and memory through a standardized protocol, supporting communication via Server-Sent Events (SSE) or standard input/output (stdio). The project facilitates creating intelligent assistants that can understand language, perform tasks, and remember context by combining Semantic Kernel's capabilities with MCP's interoperability.