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.).

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Key Building Blocks of SK

Concept	Beginner Explanation
Kernel	The central piece that connects everything. It talks to the AI model and runs your instructions.
Plugins / Skills	These are like apps for your assistant. For example: a calendar plugin, or an email plugin.
Prompt Templates	Prewritten instructions that tell the AI how to behave. You give it some variables, it fills in the rest. Like: "Summarize this: {{text}}."
Memory	It lets your assistant "remember" past things—like past conversations, or data.
Planner / Orchestrator	If your assistant has to do 5 steps to finish something (e.g. plan a trip), this part figures out the steps.

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)

Feature	Semantic Kernel (SK)	Model Context Protocol (MCP)
What it is	A toolkit/framework to build intelligent AI agents	A standard/protocol for exposing tools to LLMs
Who created it	Microsoft	Open standard (inspired by tools like OpenAI tool calling, Claude tool use)
Main Job	Let you connect an AI model with memory, tools, and prompts	Let AI agents talk to tools via a common format
Think of it as	The brain + logic + app store of an AI agent	The interface/plug for tools to work with any AI agent
Does it have memory?	Yes, SK can store and recall things (e.g. vector memory)	No, it doesn't handle memory itself
Tool usage	You define and load "skills" (functions/tools) in code	Tools are exposed as MCP Servers, any agent can connect
Language focus	Python / C# SDKs	Language-agnostic (focuses on the format, not the implementation)
Complexity	Higher-level abstraction (like a full toolkit)	Lower-level protocol (like a wire format or spec)

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

Feature	SSE	stdio
Type	Web protocol	Local program communication
Used for	Streaming real-time data from servers	Talking to command-line tools or subprocesses
Agent use	When MCP tool is running as a web server	When MCP tool is a local process
Connection	Client keeps a live HTTP connection open	Input/output pipes (like running a script)
Example	Live chat, streaming tokens from LLM	Shell script reading a prompt and printing answer

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