Creating a Model Context Protocol (MCP) server in C# enables seamless integration between AI systems and applications by standardizing communication. This guide walks you through everything from initial setup to advanced tool implementation using the official C# SDK.

Introduction to Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard designed to support context-aware communication between AI models and external systems. Its modular architecture allows developers to:

• Expose AI capabilities as reusable tools
• Handle high-throughput requests efficiently
• Maintain secure, versioned API endpoints
• Integrate easily with existing infrastructure

Since its release by Anthropic in 2024, MCP has gained rapid adoption, with Microsoft integrating it into their AI toolchain and releasing the official ModelContextProtocol C# SDK.

Prerequisites

Before you begin building your MCP server, ensure you have the following:

1. Development Environment

• .NET SDK 9.0+
• Visual Studio 2025 or VS Code with C# Dev Kit
• CLI tools for .NET and NuGet

2. Required NuGet Packages

dotnet add package ModelContextProtocol --prerelease
dotnet add package Microsoft.Extensions.Hosting

3. Foundational Knowledge

• Dependency injection in .NET
• Asynchronous programming (async/await)
• REST API design principles

Step 1: Project Setup

Create and initialize a new console application:

dotnet new console -n McpDemoServer
cd McpDemoServer

Configure the project file (McpDemoServer.csproj) with appropriate settings:

<PropertyGroup>
  <Nullable>enable</Nullable>
  <ImplicitUsings>enable</ImplicitUsings>
  <LangVersion>latest</LangVersion>
</PropertyGroup>

Step 2: Core Server Implementation

Edit Program.cs to set up the base MCP server:

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Microsoft.Extensions.Logging;
using ModelContextProtocol.Server;
using System.ComponentModel;

var builder = Host.CreateApplicationBuilder(args);

builder.Logging.AddConsole(options => 
    options.LogToStandardErrorThreshold = LogLevel.Trace);

builder.Services
    .AddMcpServer()
    .WithStdioServerTransport()
    .WithToolsFromAssembly();

await builder.Build().RunAsync();

Key Components

• AddMcpServer(): Initializes the MCP server infrastructure
• WithStdioServerTransport(): Enables communication via standard I/O
• WithToolsFromAssembly(): Automatically registers tools from the assembly

Step 3: Implementing MCP Tools

Create a new file Tools/ExampleTools.cs:

using ModelContextProtocol.Server;
using System.ComponentModel;

[McpServerToolType]
public static class DemoOperations
{
    [McpServerTool(Description = "Converts text to uppercase")]
    public static string Uppercase(string input) 
        => input.ToUpperInvariant();

    [McpServerTool(Description = "Generates Fibonacci sequence")]
    public static IEnumerable<long> Fibonacci(int count)
    {
        long a = 0, b = 1;
        for (int i = 0; i < count; i++)
        {
            yield return a;
            (a, b) = (b, a + b);
        }
    }
}

Step 4: API and Database Integration

For HTTP-based communication and database integration:

API Endpoint Configuration

builder.Services.AddMcpServer()
    .WithHttpServerTransport("https://localhost:5000/mcp")
    .WithJwtAuthentication();

Entity Framework Example

builder.Services.AddDbContext<AppDbContext>(options =>
    options.UseSqlServer(configuration.GetConnectionString("Default")));

[McpServerTool]
public class UserTools(AppDbContext context)
{
    [Description("Gets user by ID")]
    public User? GetUser(Guid id) 
        => context.Users.Find(id);
}

Step 5: Testing and Validation

Using the Built-in MCP Client

var client = new McpClient(new HttpTransport("http://localhost:5000/mcp"));

var response = await client.InvokeMethodAsync(
    "Uppercase", new { input = "test message" });

Console.WriteLine(response); // Output: TEST MESSAGE

Curl Example

curl -X POST http://localhost:5000/mcp/tools/Uppercase \
  -H "Content-Type: application/json" \
  -d '{"input":"hello world"}'

Integration Testing

[Fact]
public async Task Uppercase_ReturnsCapitalizedString()
{
    using var host = await StartTestServer();
    var client = host.GetMcpClient();

    var result = await client.InvokeMethodAsync("Uppercase", new { input = "test" });

    Assert.Equal("TEST", result);
}

Production Considerations

Monitoring

• Integrate OpenTelemetry
• Use Application Insights
• Track QPS, latency, and errors

Security

• Enforce TLS 1.3
• Implement OAuth2 scopes
• Apply rate limits via API gateways

Performance

services.AddMcpServer()
    .WithOptimizedBinaryTransport()
    .WithConnectionPooling(100);

Real-World Use Cases

AI-Powered CRM

• Integrate Salesforce with LLMs
• Auto-summarize meetings
• Predict deal closures

Smart Manufacturing

• Link IoT sensor data with models
• Schedule production tasks
• Monitor machine health

Healthcare Analytics

• Analyze medical images
• Generate risk profiles
• Automate insurance codes

Troubleshooting Common Issues

Future-Proofing Your Implementation

Versioning Strategy

services.AddMcpServer()
    .WithVersioning(v => 
    {
        v.AddEndpoint("v1", api => api.WithToolsFromAssembly());
        v.AddEndpoint("v2", api => api.WithNewFeatures());
    });

Hybrid Transport

.WithHttpServerTransport("http://*:5000")
.WithGrpcServerTransport("http://*:5001")

Integration with Semantic Kernel

var kernel = Kernel.CreateBuilder()
    .AddMcpPlugin("http://localhost:5000/mcp")
    .Build();

Conclusion

Building an MCP server in C# provides a solid foundation for scalable, secure AI integration. With the ModelContextProtocol SDK, you can expose AI tools, handle large request volumes, and ensure long-term maintainability.

Whether you're building internal tools or customer-facing AI solutions, MCP is a powerful protocol to include in your stack.