My previous post showed how easy it is to develop a .NET C# MCP server and write a client able to communicate with it. Now, the question is how we can distribute our MCP server to be used by AI clients. In this post, we'll leverage the knowledge acquired from my previous posts to explore how to dockerize your .NET C# MCP server.
In my previous post, I demonstrated how to use C# with Microsoft.Extensions.AI, Ollama, and a nuget package called mcpdotnet to interact with an existing MCP Server. Since then, mcpdotnet has been elevated to become the "official C# SDK for Model Context Protocol servers and clients, maintained by Microsoft".
The Model Context Protocol (MCP) is an open standard that enables seamless integration between AI systems and various data sources, allowing developers to create context-aware applications. In this post, we'll explore how to build a simple MCP server using C# and the modelcontextprotocol / csharp-sdk library, which simplifies the process of creating and managing MCP servers.
In the previous post "Leveraging Microsoft.Extensions.AI for Tool Calling in C#", we explored how to create custom tools that enhance the capabilities of Large Language Models (LLMs). We demonstrated how integrating these technologies enables developers to build applications with advanced AI capabilities, facilitating more complex interactions.
In this post, we'll take a step further and explore how to leverage Model Context Protocol (MCP) servers to utilize external Tools and AI models in C# applications. We'll continue using Ollama to run local AI models.
In my previous post, we saw how to build the simplest Semantic Kernel local AI agent using Semantic Kernel and Ollama in C#. In this short post, we will see how simple it is to extend the capabilities of the Semantic Kernel local AI agent by adding function calling.
