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, enabling seamless interaction between AI models and external data sources or tools. By following this guide, you'll learn how to connect to an MCP server, convert MCP tools into Semantic Kernel functions, and leverage large language models (LLMs) for function calling—all within a reusable and extensible framework.

Quick Start - Running the Demo

Prerequisites

• .NET SDK (version 9.0 or later - uses preview version features)
• Node.js and npm (optional - only required for external MCP servers like GitHub and Everything)
• OpenAI API key (optional - only needed for Semantic Kernel integration)
• ModelContextProtocol NuGet package (version 0.1.0-preview.8 - automatically included)
• Basic familiarity with C# and Semantic Kernel concepts

Note: Node.js is needed only for external MCP servers (GitHub, Everything) that are published as Node.js packages. The demo works perfectly with just the local .NET MCP server, avoiding Node.js dependencies entirely.

1. Clone and Build

git clone https://github.com/LiteObject/mcp-with-semantic-kernel.git
cd mcp-with-semantic-kernel
dotnet restore
dotnet build

2. Configure (Optional)

Set up your OpenAI API key for Semantic Kernel integration (this is optional):

dotnet user-secrets set "OpenAI:ApiKey" "your-api-key-here" --project src/Demo.MCP.Client

3. Run the MCP Client Demo

dotnet run --project src/Demo.MCP.Client

The application will:

1. Automatically connect to enabled MCP servers (configured in appsettings.json)
2. List available tools from each connected server (see Local MCP Server Tools section)
3. Demonstrate tool execution with sample calls (Echo tool test)
4. Start interactive mode where you can test commands

4. Quick Test with Local Server Only

For the simplest setup that doesn't require Node.js, the demo is pre-configured to use only the local .NET MCP server. Just run:

# The client (already configured to use local server)
dotnet run --project src/Demo.MCP.Client

The local server includes comprehensive testing tools (see Local MCP Server Tools section).

5. Interactive Commands

Once the client is running, try these commands:

> servers                           # List connected servers  
> list local                        # List tools for local server
> call local Add {"a": 5, "b": 3}   # Test calculator tool (note: case-sensitive "Add")
> call local Echo {"name": "World"} # Test echo tool (note: case-sensitive "Echo")
> call local GetDateTime {}         # Get current time (no parameters needed)
> exit                              # Quit the application

Important Notes:

• Tool names are case-sensitive (use "Add", not "add")
• Parameters must be valid JSON format
• Use the exact parameter names as defined in the tool (e.g., "a" and "b" for Add tool)

6. Enable External Servers (Optional)

To test with GitHub and Everything servers, edit src/Demo.MCP.Client/appsettings.json to enable them. Important: Only change the "Enabled" property to true for each server you want to activate - do not replace the entire configuration objects.

Example change (see Configuration section for full server settings):

{
  "McpServers": {
    "github": { 
      "Enabled": true    // Change this from false to true
      // Keep all other properties unchanged
    },
    "everything": { 
      "Enabled": true    // Change this from false to true  
      // Keep all other properties unchanged
    },
    "local": { "Enabled": true }
  }
}

Then restart the client. This requires Node.js and internet access.

What is Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open-standard protocol designed to standardize how applications provide context to AI models. It acts as a universal connector, allowing LLMs to interact with diverse data sources (e.g., APIs, databases, or services) in a consistent way. Think of MCP as a bridge that enhances AI interoperability, flexibility, and contextual understanding.

In this project, we use MCP to expose tools that Semantic Kernel can consume, enabling AI-driven workflows with real-world applications like automation, data retrieval, or system integration.

Why Use Semantic Kernel with MCP?

Microsoft Semantic Kernel is a powerful SDK that simplifies building AI agents and orchestrating complex workflows. By integrating MCP tools, you can:

• Extend Semantic Kernel with external capabilities via MCP servers.
• Enable LLMs to call functions dynamically based on user prompts.
• Promote interoperability between AI models and non-Semantic Kernel applications.
• Simplify development with a standardized protocol for tool integration.

This repository provides a practical example of how to combine these technologies, complete with sample code to get you started.

Project Structure

src/
├── Demo.MCP.Client/              # MCP client application  
│   ├── Configuration/            # Robust configuration system with validation
│   │   ├── AppConfig.cs         # Main configuration class with smart loading
│   │   ├── McpServerConfig.cs   # MCP server configuration with transport types
│   │   └── OpenAIConfig.cs      # OpenAI/Semantic Kernel configuration
│   ├── Services/                # Business logic and MCP integration
│   │   ├── LoggingService.cs    # Structured logging with Serilog
│   │   └── McpClientService.cs  # MCP client with retry logic and error handling
│   ├── Program.cs               # Main application with interactive demo
│   └── appsettings.json         # Configuration for servers, logging, and OpenAI
└── Demo.MCP.Server/             # Local MCP server (.NET implementation)
    ├── Tools/                   # 23 example MCP tools organized by category
    │   ├── CalculatorTool.cs    # Math operations (Add, Subtract, Multiply, etc.)
    │   ├── EchoTool.cs          # Simple echo functionality for testing
    │   ├── FileSystemTool.cs    # File operations (FileExists, ListFiles, etc.)
    │   ├── SystemInfoTool.cs    # System information and environment tools  
    │   └── TextProcessingTool.cs # Text manipulation (Base64, regex, etc.)
    └── Program.cs               # MCP server using stdio transport

Step-by-Step Guide

This section walks you through the process of integrating MCP tools with Semantic Kernel, as implemented in this repository.

Step 1: Set Up Your Project

1. Clone this repository:

   git clone https://github.com/LiteObject/mcp-with-semantic-kernel.git
   cd mcp-with-semantic-kernel
   

2. Restore dependencies:

   dotnet restore
   

3. Configure your OpenAI API key (optional - see Prerequisites for when this is needed):

   dotnet user-secrets set "OpenAI:ApiKey" "your-api-key" --project src/Demo.MCP.Client
   dotnet user-secrets set "OpenAI:ChatModelId" "gpt-4o" --project src/Demo.MCP.Client
   

Step 2: Connect to an MCP Server

The project includes code to connect to an MCP server using the ModelContextProtocol package. The MCP client retrieves available tools from the server, which can then be used by Semantic Kernel.

Conceptual example (actual implementation in src/Demo.MCP.Client/Services/McpClientService.cs):

using ModelContextProtocol;

var mcpConfig = new McpServerConfig
{
    Id = "everything",
    Name = "Everything",
    TransportType = TransportType.Stdio,
    Command = "npx",
    Arguments = ["-y", "@modelcontextprotocol/server-everything"]
};

var mcpClient = await McpClientFactory.CreateAsync(mcpConfig);
var tools = await mcpClient.ListToolsAsync();

This snippet demonstrates the general pattern for establishing a connection to an MCP server (e.g., the "Everything" demo server) and fetching its available tools. The actual implementation includes additional error handling, retry logic, and configuration management.

Configuration

The application uses a sophisticated configuration system with validation and smart path resolution:

Automatic MCP Server Management

The client automatically starts and manages the local MCP server using relative paths:

{
  "McpServers": {
    "local": {
      "Id": "local",
      "Name": "Local Demo Server", 
      "TransportType": "Stdio",
      "Command": "dotnet",
      "Arguments": ["run", "--project", "src/Demo.MCP.Server"],
      "ConnectionTimeout": "00:00:30",
      "MaxRetries": 3,
      "Enabled": true
    }
  }
}

Available MCP Servers

• Local Server: .NET MCP server with comprehensive built-in tools (enabled by default)
• GitHub Server: Provides GitHub API access (disabled by default)
• Everything Server: Demo server with various tools (disabled by default)

Configuration Features

• Hierarchical loading: appsettings.json, environment-specific files, user secrets
• Validation: Comprehensive validation with descriptive error messages
• Logging configuration: Structured logging with Serilog, file and console output
• Retry logic: Configurable connection timeouts and retry attempts

Transport Options for MCP Communication

MCP supports different transport mechanisms for client-server communication. Understanding these options helps you choose the right configuration for your use case.

Available Transport Types

1. Stdio (Standard Input/Output)

The default and most common transport for local MCP servers.

How it works: The client starts the server as a subprocess and communicates through stdin/stdout pipes.

Configuration example:

{
  "TransportType": "Stdio",
  "Command": "dotnet",
  "Arguments": ["run", "--project", "src/Demo.MCP.Server"]
}

Use cases:

• Local development and testing
• Embedded servers within applications
• Scenarios where the client controls the server lifecycle

Advantages:

• Simple setup, no network configuration needed
• Secure (no network exposure)
• Client automatically manages server lifecycle

Limitations:

• Server must be on the same machine as client
• One client per server instance

2. HTTP/HTTPS (Not Implemented in This Demo)

Network-based transport for remote MCP servers.

How it works: Client connects to a server exposed via HTTP endpoint.

Potential configuration:

{
  "TransportType": "Http",
  "Endpoint": "https://api.example.com/mcp"
}

Use cases:

• Cloud-hosted MCP servers
• Multi-client scenarios
• Microservices architecture

Advantages:

• Remote server access
• Multiple clients can connect to one server
• Standard web infrastructure (load balancers, proxies, etc.)

Limitations:

• Requires network setup and security considerations
• Higher latency than local transports

3. WebSocket (Not Implemented in This Demo)

Real-time bidirectional communication.

Use cases:

• Real-time updates from server to client
• Long-running connections
• Push notifications

4. Named Pipes (Not Implemented in This Demo)

Inter-process communication on the same machine.

Use cases:

• High-performance local communication
• Windows services integration

Choosing the Right Transport

Scenario	Recommended Transport	Why
Local development	Stdio	Simple, no configuration needed
Production server	HTTP/HTTPS	Scalable, standard infrastructure
Real-time features	WebSocket	Bidirectional communication
Windows service	Named Pipes	Native Windows IPC

Current Implementation

This demo uses Stdio transport for all servers:

• Local server: Started as subprocess via dotnet run
• GitHub server: Started via npx command
• Everything server: Started via npx command

The Stdio transport was chosen for simplicity and security, as it doesn't expose any network endpoints and works consistently across platforms.

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Links

• MCP Servers
• Model Context Protocol Specification
• Microsoft Semantic Kernel