Posts tagged with "MCP"

In my first post about TypeSafe and Jev in .NET, I used the SDK to make structured judgments about text and JSON: Noul for a yes-or-no probability, Choice for an unordered selection, and Score for an ordinal rubric. In the follow-up about the MCP server, I showed how to make those same judgments available to an AI coding agent.

TypeSafe's JavaScript SDK v0.7.0 adds experimental image input and a box question type. "I" ported those features to my community .NET SDK in its own 0.7.0 update. The version numbers match, but these are separate releases: TypeSafe published the JavaScript SDK v0.7.0 on October 14, 2026, while the .NET SDK remains an unofficial community port.

Two weeks ago I introduced my unofficial TypeSafe .NET SDK and used it to ask Jev several typed questions about a support ticket. My C# application controlled the workflow; Jev returned structured answers for the judgments it needed.

This week I wanted to ask those same questions from any AI coding agent. I built TypeSafe.Mcp, a separate .NET 11 application that uses the SDK under the hood. It exposes five tools over stdio, and any MCP client can now interact with Jev through these tools.

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.