MCP C# SDK Guide: Building Model Context Protocol Clients and Servers with .NET

What Is MCP C# SDK

The MCP C# SDK is the official .NET implementation of the Model Context Protocol (MCP), designed to help developers integrate large language models (LLMs) with external tools, services, and data sources.

Model Context Protocol is an open standard that defines how applications provide context and capabilities to AI models. Instead of building custom integrations for each API or tool, MCP introduces a unified interface that allows AI systems to access tools, resources, and prompts in a consistent way.

In practical terms, MCP acts as a middleware layer between AI models and software systems. An AI agent can discover available tools, call them, retrieve data, and execute tasks through the MCP protocol without needing direct integration with each service.

The C# SDK enables .NET developers to implement MCP-compatible clients and servers. It allows applications, services, and libraries built with C# to participate in the MCP ecosystem and interact with AI tools seamlessly.

The SDK is maintained in collaboration with the MCP open protocol community and Microsoft, and it is distributed via NuGet for easy integration into .NET projects.

Core Architecture of MCP C# SDK

The MCP ecosystem follows a client–server architecture where AI applications interact with external tools through MCP servers.

• An MCP Server exposes tools, resources, and prompts that AI systems can use.
• An MCP Client connects to a server and invokes these capabilities.

The C# SDK provides full protocol support and type-safe APIs for implementing both sides of this architecture.

The official SDK consists of several packages designed for different use cases:

• ModelContextProtocol: This is the main package and contains the core implementation along with hosting and dependency injection extensions. Most applications only need this package.
• ModelContextProtocol.AspNetCore: This library is designed for building HTTP-based MCP servers using ASP.NET Core.
• ModelContextProtocol.Core: This package contains low-level protocol implementations for lightweight clients or custom server implementations with minimal dependencies.

The SDK integrates well with modern .NET development patterns such as dependency injection, asynchronous programming, and the Microsoft.Extensions ecosystem.

Creating an MCP Client in C#

One of the main use cases of the SDK is building a client that can connect to an MCP server and invoke its tools.

To install the SDK, add the package from NuGet:

dotnet add package ModelContextProtocol

After installing the package, a client can be created by establishing a transport connection and initializing an McpClient.

Example:

using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol;

var clientTransport = new StdioClientTransport(new StdioClientTransportOptions
{
    Name = "Everything",
    Command = "npx",
    Arguments = ["-y", "@modelcontextprotocol/server-everything"],
});

var client = await McpClient.CreateAsync(clientTransport);

// List available tools
foreach (var tool in await client.ListToolsAsync())
{
    Console.WriteLine($"{tool.Name} ({tool.Description})");
}

// Call a tool
var result = await client.CallToolAsync(
    "echo",
    new Dictionary<string, object?>()
    {
        ["message"] = "Hello MCP!"
    });

Console.WriteLine(result);

Once connected, the client can enumerate tools provided by the server and execute them programmatically. This mechanism is often integrated with AI frameworks so that LLMs can automatically call tools during conversations.

Because MCP is protocol-based, a C# client can connect to MCP servers written in other languages such as Python, TypeScript, or Go.

Creating an MCP Server in C#

Developers can also use the SDK to build servers that expose tools to AI agents.

A simple MCP server can be created using the .NET hosting model:

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol.Server;

var builder = Host.CreateApplicationBuilder(args);

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

await builder.Build().RunAsync();

Tools can then be defined as C# methods and exposed to MCP clients:

[McpServerToolType]
public static class EchoTool
{
    [McpServerTool]
    public static string Echo(string message)
    {
        return $"hello {message}";
    }
}

Once the server is running, any MCP-compatible client or AI agent can discover and invoke the Echo tool.

This design allows developers to easily expose APIs, scripts, or business logic as AI-accessible tools.

Common Use Cases for MCP C# SDK

As AI agents become more capable, the MCP protocol is increasingly used to connect LLMs with real-world systems.

The MCP C# SDK is particularly useful in scenarios such as:

• AI agent development, where models automatically call external tools to complete tasks.
• Enterprise AI integration, allowing internal systems like CRM, ERP, or databases to be accessed by AI assistants.
• Automation workflows, where AI orchestrates APIs, services, and data pipelines.
• Cross-language tool ecosystems, where C# services interact with AI agents built in Python or JavaScript.

Because MCP standardizes tool interfaces, developers can reuse existing MCP servers and avoid building custom integrations for every AI workflow.

Conclusion

The MCP C# SDK provides a powerful foundation for integrating .NET applications with the rapidly growing AI agent ecosystem. By implementing the Model Context Protocol, developers can build standardized tools, services, and clients that interact seamlessly with large language models.

With support for both MCP clients and servers, along with modern .NET features like dependency injection and asynchronous APIs, the SDK makes it easier than ever to connect enterprise systems, APIs, and data sources to AI-driven workflows.

As MCP adoption continues to grow, learning how to use the MCP C# SDK will become an increasingly valuable skill for developers building next-generation AI applications.