What is Mcp Server With Semantic Kernel

mcp-server-with-semantic-kernel is a framework for building Semantic Kernel agents using Model Context Protocol (MCP) plugins in Python. It enables the creation of AI agents that can communicate, perform tasks, and learn from interactions.

Use cases

Use cases include developing a Meeting Assistant that schedules appointments, creating a Customer Support Agent that answers queries, and building an Educational Tutor that provides personalized learning experiences.

How to use

To use mcp-server-with-semantic-kernel, developers can integrate it into their Python applications by utilizing the provided plugins and following the guidelines in the documentation. Users can create prompts and manage workflows to enhance the capabilities of their AI agents.

Key features

Key features include a central Kernel for managing interactions, various plugins for specific tasks (like calendar management), prompt templates for structured instructions, memory for retaining past interactions, and a planner/orchestrator for managing complex workflows.

Where to use

mcp-server-with-semantic-kernel can be used in various fields such as personal assistants, customer service bots, educational tools, and any application that requires intelligent task management and interaction.

mcp-server-with-semantic-kernel

Building Semantic Kernel Agents with Model Context Protocol (MCP) Plugins in Python

Using MCP Servers with Semantic Kernel in Python

What is Semantic Kernel?

Semantic Kernel (SK) is an open-source toolkit created by Microsoft that helps developers build powerful AI agents and apps that can talk, think, and act.

Think of it like this:

Imagine you’re building a robot assistant. This assistant can:

1. Talk using ChatGPT or other AI models (like asking questions or answering)
2. Do tasks like checking your calendar, sending emails, or summarizing documents
3. Remember things it has done or seen before
4. Learn and follow workflows you teach it

SK is the brain behind this assistant. It connects the language part (like ChatGPT) with the tools and memory part (your calendar, files, APIs, etc.).

Key Building Blocks of SK

Example in Simple Terms

Let’s say you’re building a “Meeting Assistant” using Semantic Kernel:

• You give it a plugin called CalendarSkill that knows how to read and create calendar events
• You write a prompt: “Schedule a 30-minute meeting with John sometime next week.”
• The Semantic Kernel takes your prompt, sends it to the AI (like ChatGPT), figures out the intent (“create event”), and then runs the plugin to do it

So SK = AI (understands language) + Tools (do actions) + Memory (remembers stuff)

Semantic Kernel (SK) vs Model Context Protocol (MCP)

Communication Methods

1. Server-Sent Events (SSE)

TL;DR: SSE is a way for a server to send real-time updates to your app over the web.

Think of it like:
You’re at a coffee shop. You give the barista your order (a request), and instead of checking back constantly, you just sit and relax. When your drink is ready, they call your name.

How it works:

• The client makes a one-time request
• The server keeps that connection open
• Whenever there’s data, the server sends it as a stream
• The client gets updates in real time

In MCP or AI apps:
When an AI tool talks to a tool server (via MCP), the server may stream results gradually — like partial answers, tokens, or logs.

2. Standard Input/Output (stdio)

TL;DR: stdio is how your computer’s command-line tools read input and write output.

How it works:

• stdin: You type or send data into a program
• stdout: The program prints the result
• stderr: Errors go here (so they don’t mix with normal output)

In MCP:
MCP Servers can be run as command-line programs. The agent talks to them by:

• Writing JSON input into stdin
• Reading the tool’s response from stdout

Communication Methods Comparison

In AI Tooling (like MCP):

• If a tool is deployed on a web server → use SSE to stream responses
• If a tool is a local binary/script → use stdio to send/receive data