Integrating MCP Tools with Semantic Kernel: A Step-by-Step Guide

What is Integrating Model Context Protocol (MCP) Tools with Semantic Kernel: A Step-by-Step Guide?

This repository demonstrates how to integrate Model Context Protocol (MCP) tools with Microsoft Semantic Kernel, allowing AI models to interact with external data sources and tools through a standardized interface. It provides a reusable .NET framework with a local MCP server, client application, and configuration system for developers building AI-driven workflows.

How to use Integrating Model Context Protocol (MCP) Tools with Semantic Kernel: A Step-by-Step Guide?

Clone the repository, restore .NET dependencies, optionally set an OpenAI API key via user secrets, and run the demo client with dotnet run --project src/Demo.MCP.Client. The client automatically connects to enabled MCP servers (configured in appsettings.json) and starts an interactive command mode where you can list tools and call them with JSON parameters.

Key features of Integrating Model Context Protocol (MCP) Tools with Semantic Kernel: A Step-by-Step Guide

• Automatically connects to MCP servers and converts their tools into Semantic Kernel functions
• Includes a local .NET MCP server with 23 example tools (calculator, echo, file system, system info, text processing)
• Supports multiple MCP transports (Stdio by default; HTTP/HTTPS, WebSocket, Named Pipes are discussed but not implemented)
• Robust configuration with validation, retry logic, and structured logging via Serilog
• No Node.js required when using only the local server

Use cases of Integrating Model Context Protocol (MCP) Tools with Semantic Kernel: A Step-by-Step Guide

• Extend Semantic Kernel with external capabilities such as file system access or API calls
• Enable large language models to dynamically invoke tools based on user prompts
• Test and prototype MCP tool integration in a controlled local environment
• Build reusable AI agents that can interact with multiple data sources through a single protocol