Offline LLMs: Build Smarter AI with MCP Servers

Описание: In this follow-up to building a basic MCP server in .NET, I’ll show you how to take your AI integration to the next level by reliably connecting your LLM to a local MCP server with proper contracts, serialization, and a fallback strategy.

We’ll break down:

✅ How to extract structured JSON from LLM output
✅ Deserialize and pass data to your tools using MCPClient
✅ Design a robust fallback system to handle unpredictable LLM responses
✅ A simple and powerful architecture to run local LLMs with MCP servers — no cloud needed!

🔧 Code Highlights:
We’re using:
Mistral LLM .gguf model (offline)
JSON extraction and validation logic
Custom tool execution through MCP
A well-structured McpRoot schema for deserialization

You'll see the entire C# implementation that connects an offline Mistral model with a simple MCP server and gets actionable results like:

“Give me the outcome of 22 added to 55.”
→ Processed, structured, and executed locally.

🧱 Architecture Overview:
The video includes a walk-through of the system design — showing how an LLM interfaces with:

Your MCP server
Contracts to enforce consistent output
Tool definitions for local logic

Resilience mechanisms to keep things running smoothly, even when the LLM fails to format output correctly

💬 Drop a comment if you want the repo, have questions, or want to suggest improvements.
👍 Like the video if this architecture inspires you, and subscribe for more practical AI tutorials.

Here's some useful links:
Previous MCP Server in .NET Video:
   • Build a Simple MCP Server & Client in C#.NET  

Source Code:
https://github.com/hassanhabib/Simple...


#LLM #MCP #OfflineAI #DotNet #AIEngineering #Mistral #CSharp #LocalAI #DevTools