Skip to main content

Tool calling using MCP with Semantic Kernel

Semantic Kernel has the concept of a plugin as a way to give your AI superpowers and allow it to perform actions that it wouldn’t be able to do otherwise. A plugin consists of one or more functions that are available to your AI to be invoked.

In Semantic Kernel you can create native plugins that are written in C#, Java or Python or create a plugin based on an OpenAPI specification. Behind the scenes, Semantic Kernel leverages function calling (also called tool calling). With function calling, LLMs can request a particular function, invoke it and capture the results.

The way that these tools are created and called can be different from AI to AI and platform to platform. To tackle this problem Anthropic created the Model Context Protocol as a way to standardize all these integrations.

Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

The team behind Semantic Kernel didn’t hesitate and added support for MCP based plugins as well (currently in preview). Let me show you how to use it…

Using an MCP server as a plugin in Semantic Kernel

The first thing you need is an MCP server. Today there are typically 2 ways that an MCP server can exchange information:

  • Standard Input Output (STDIO)
  • Server Send Events(SSE)

I will be using SSE in this example with the Playwright MCP server.

  • Start the server by executing the following command:

npx @playwright/mcp@latest --port 8931

  • Now open your project(I assume you already have the basic Semantic Kernel setup done) and add the following NuGet package:

dotnet add ModelContextProtocol-SemanticKernel

  • Next step is to load the tools provided by the MCP server as a plugin:
  • Last important step is that we enable automatic function invocation on the AI model so that it can invoke the available tools.
  • Now we can interact with our AI model as before:

That’s all!

For an end-to-end demo, check out my Semantic Kernel demo application: wullemsb/SemanticKernel at MCP

More information

Introduction - Model Context Protocol

Semantic Kernel – Auto function calling

wullemsb/SemanticKernel at MCP

microsoft/playwright-mcp: Playwright MCP server

Plugins in Semantic Kernel | Microsoft Learn

Let GitHub Copilot interact with your local PowerPoint and Word documents

Labels:

Popular posts from this blog

Azure DevOps/ GitHub emoji

I’m really bad at remembering emoji’s. So here is cheat sheet with all emoji’s that can be used in tools that support the github emoji markdown markup: All credits go to rcaviers who created this list.
Read more

Kubernetes–Limit your environmental impact

Reducing the carbon footprint and CO2 emission of our (cloud) workloads, is a responsibility of all of us. If you are running a Kubernetes cluster, have a look at Kube-Green . kube-green is a simple Kubernetes operator that automatically shuts down (some of) your pods when you don't need them. A single pod produces about 11 Kg CO2eq per year( here the calculation). Reason enough to give it a try! Installing kube-green in your cluster The easiest way to install the operator in your cluster is through kubectl. We first need to install a cert-manager: kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.14.5/cert-manager.yaml Remark: Wait a minute before you continue as it can take some time before the cert-manager is up & running inside your cluster. Now we can install the kube-green operator: kubectl apply -f https://github.com/kube-green/kube-green/releases/latest/download/kube-green.yaml Now in the namespace where we want t...
Read more

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...
Read more