Posts tagged with "SLM"

As developers, we often find ourselves tied to specific providers. The same applies to Large Language Model (LLM) providers. This can limit our flexibility and control over our applications. In this blog post, we'll explore how to decouple ourselves from LLM commodities by leveraging Dapr's Conversation building block. This approach allows us to switch between different LLM providers seamlessly, ensuring that our applications remain adaptable and future-proof.

Now that we've explored how to leverage Model Context Protocol (MCP) servers to utilize external Tools and AI models in C# applications, how to write your own Standard Input/Output (STDIO) MCP server in C# using the modelcontextprotocol / csharp-sdk, and how we can dockerize your .NET C# MCP server to be able to distribute it for use by AI clients. It's time to take a step further and explore how to leverage Server-Sent Events (SSE) MCP servers so that we can deploy them remotely, for example on a Raspberry Pi.

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.