Building Production-Ready MCP Servers in ASP.NET Core 10

The Model Context Protocol (MCP) has quickly become one of the most important standards for integrating AI assistants with external tools, APIs, and enterprise systems. Instead of building custom integrations for every AI application, MCP provides a standardized way for AI agents to discover capabilities and invoke tools.

While creating a simple MCP server is relatively straightforward, building one that is secure, scalable, observable, and production-ready requires additional architectural considerations. This article walks through the process of building an MCP server in ASP.NET Core 10 while covering authentication, dependency injection, structured logging, validation, and deployment best practices.

Understanding MCP Servers

What Is an MCP Server?

An MCP server exposes tools, resources, and prompts that AI assistants can consume through a standardized protocol. Instead of exposing traditional REST endpoints directly to AI agents, the MCP server acts as an intelligent bridge between enterprise services and AI applications.

Typical responsibilities include:

Exposing business capabilities as AI tools
Validating incoming requests
Executing backend services
Returning structured responses
Managing authentication and authorization

For enterprise applications, this separation helps prevent AI clients from directly accessing internal APIs.

Why ASP.NET Core 10 Is a Good Choice

ASP.NET Core 10 offers several advantages for hosting MCP servers:

High-performance request processing
Built-in dependency injection
Native OpenTelemetry integration
Minimal API support
Modern authentication middleware
Cloud-native deployment capabilities
Excellent container support

These features make it suitable for production AI workloads that require scalability and maintainability.

Project Architecture

A production-ready MCP server should separate protocol handling from business logic.

AI Client
     │
     ▼
MCP Server
     │
 ┌───────────────┐
 │ Tool Handlers │
 └───────────────┘
     │
Business Services
     │
Repositories
     │
Database / External APIs

Keeping these layers separate allows business services to remain reusable outside the MCP implementation.

Creating the ASP.NET Core 10 Project

Create a new Web API project.

dotnet new webapi -n ProductCatalogMcp

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddScoped<IProductService, ProductService>();

builder.Services.AddOpenApi();

var app = builder.Build();

app.MapOpenApi();

app.Run();

This setup keeps service registration centralized and enables dependency injection throughout the application.

Implementing an MCP Tool

Suppose an AI assistant needs to retrieve product information.

Create a service interface.

public interface IProductService
{
    Task<ProductDto?> GetProductAsync(int id);
}

Implementation:

public class ProductService : IProductService
{
    public Task<ProductDto?> GetProductAsync(int id)
    {
        return Task.FromResult<ProductDto?>(new ProductDto
        {
            Id = id,
            Name = "Laptop",
            Price = 1499
        });
    }
}

Expose the tool.

app.MapPost("/tools/product", async (
    int id,
    IProductService service) =>
{
    var product = await service.GetProductAsync(id);

    return product is null
        ? Results.NotFound()
        : Results.Ok(product);
});

Although this example is simple, the important design choice is keeping the endpoint thin. The endpoint delegates business logic to a service instead of embedding logic directly in the request handler, making the application easier to test and maintain.

Building a Real-World Workflow

Consider an internal inventory system where an AI assistant helps support engineers.

The workflow might look like this:

The AI agent receives a user request.
The agent discovers available MCP tools.
It invokes the Product Lookup tool.
The MCP server validates the request.
The business service retrieves inventory data.
The server returns structured JSON.
The AI assistant summarizes the result for the user.

This approach keeps enterprise APIs isolated while allowing AI agents to perform useful business tasks.

Dependency Injection and Service Design

Avoid placing database or HTTP calls inside MCP handlers.

Instead:

MCP Endpoint
      │
      ▼
Application Service
      │
      ▼
Repository
      │
      ▼
Database

This separation improves:

Testability
Maintainability
Code reuse
Dependency management

Error Handling

Production systems should never expose raw exceptions.

app.UseExceptionHandler(errorApp =>
{
    errorApp.Run(async context =>
    {
        context.Response.StatusCode = 500;

        await context.Response.WriteAsJsonAsync(new
        {
            Error = "An unexpected error occurred."
        });
    });
});

Returning consistent error responses simplifies debugging for both AI clients and developers while preventing accidental exposure of internal implementation details.

Logging and Observability

Structured logging is essential for diagnosing issues in AI-driven workflows.

app.Logger.LogInformation(
    "Tool ProductLookup executed for ProductId {Id}",
    id);

Useful information to log includes:

Tool name
Execution duration
Correlation ID
User identity
Request outcome

Avoid logging sensitive prompts, secrets, or personally identifiable information.

Comparison with Traditional REST APIs

Feature	REST API	MCP Server
Primary consumer	Applications	AI agents
Tool discovery	Manual	Built-in
AI-friendly responses	Limited	Yes
Protocol standardization	Varies	Standardized
Enterprise AI integration	Custom	Native

MCP complements REST rather than replacing it. Existing APIs continue to serve applications, while MCP provides an AI-friendly layer on top.

Best Practices

Keep MCP handlers lightweight.
Delegate business logic to application services.
Validate every request before execution.
Use dependency injection throughout the application.
Implement structured logging.
Return consistent error responses.
Version tools to support future changes.
Monitor tool execution metrics.
Design tools to be idempotent whenever possible.

Common Mistakes

One common mistake is exposing internal APIs directly through MCP without proper abstraction. This tightly couples AI clients to backend implementations and makes future changes difficult.

Another issue is embedding business logic inside endpoint handlers. Keeping handlers thin improves readability, testing, and long-term maintainability.

Developers should also avoid returning inconsistent JSON structures, as AI agents rely on predictable response formats for accurate tool execution.

Testing and Validation

A production-ready MCP server should be validated before deployment.

Recommended testing includes:

Unit tests for business services
Integration tests for MCP endpoints
Authentication and authorization testing
Invalid input validation
Load testing for concurrent tool execution
End-to-end AI workflow testing

Automated integration tests help ensure that tool contracts remain stable as the application evolves.

Performance Considerations

For production workloads, performance is often influenced more by backend dependencies than by the MCP protocol itself.

To improve throughput:

Cache frequently requested data.
Use asynchronous APIs throughout the request pipeline.
Minimize unnecessary database queries.
Reuse HTTP clients through dependency injection.
Apply pagination for large datasets.
Monitor request latency with OpenTelemetry.

Keeping handlers lightweight ensures the server spends most of its time executing business logic rather than processing protocol overhead.

Security Considerations

Because MCP servers expose enterprise capabilities to AI agents, security should be treated as a primary design concern.

Consider the following practices:

Require authentication for all tool invocations.
Authorize users based on roles or policies.
Validate every input parameter.
Never expose internal exception details.
Protect secrets using secure configuration providers.
Encrypt communication using HTTPS.
Apply rate limiting to prevent abuse.
Audit tool execution for compliance.

Following the principle of least privilege helps ensure AI agents can perform only the actions they are explicitly authorized to execute.

Troubleshooting

Tool Returns 404

Verify that the endpoint is correctly mapped and that the client is calling the expected route.

Dependency Injection Errors

Ensure all required services are registered in the dependency injection container before the application starts.

Slow Tool Execution

Measure database queries, external API calls, and downstream services to identify bottlenecks. In many cases, the MCP layer is not the primary source of latency.

Authentication Failures

Confirm that authentication middleware is configured correctly and that the AI client sends valid credentials or tokens with each request.

Conclusion

Building an MCP server involves much more than exposing a few endpoints. A production-ready implementation should emphasize clean architecture, dependency injection, structured logging, robust validation, and strong security practices. ASP.NET Core 10 provides the performance, cloud-native capabilities, and modern development features needed to build reliable MCP servers that integrate seamlessly with enterprise AI applications. By treating the MCP server as an application layer rather than a thin API wrapper, developers can create scalable, maintainable, and secure AI integrations that are ready for real-world production environments.