Building an AI Coding Assistant in ASP.NET Core Using MCP Servers

Introduction

AI coding assistants have become an essential part of modern software development. While Large Language Models (LLMs) excel at generating code, they have one significant limitation—they don't have direct access to your project's files, APIs, documentation, or development tools.

This is where the Model Context Protocol (MCP) comes in. MCP provides a standardized way for AI applications to communicate with external tools and services, enabling coding assistants to retrieve project files, search documentation, inspect Git repositories, execute commands, and interact with development environments.

In this article, you'll build the foundation of an AI coding assistant in ASP.NET Core that connects to an MCP server and explore the production practices needed for enterprise applications.

What Is an MCP Server?

An MCP server acts as a bridge between an AI model and external resources.

Instead of embedding custom integrations into every AI application, the MCP server exposes tools that the AI can discover and invoke dynamically.

Common capabilities include:

Reading project files
Searching source code
Accessing Git repositories
Querying databases
Calling REST APIs
Retrieving documentation
Executing development utilities

This standardized approach makes AI applications more modular and easier to maintain.

Solution Architecture

A typical AI coding assistant consists of the following components:

Component	Responsibility
ASP.NET Core Application	Provides the user interface or API
OpenAI or Azure OpenAI	Understands user requests
MCP Client	Connects to the MCP server
MCP Server	Exposes available development tools
External Resources	Files, Git, APIs, documentation, databases

The AI model determines which tool to use, while the MCP server handles the interaction with the external resource.

Creating the MCP Client

The first step is connecting your application to an MCP server.

using ModelContextProtocol.Client;

var client = await McpClient.ConnectAsync(
    "http://localhost:3000");

This establishes communication with the MCP server and prepares the application to discover available tools.

Discovering Available Tools

Once connected, retrieve the list of supported tools.

var tools = await client.ListToolsAsync();

foreach (var tool in tools)
{
    Console.WriteLine(tool.Name);
}

An MCP server may expose tools such as:

File Search
Repository Explorer
Documentation Search
Terminal Commands
Database Query
Code Analyzer

Rather than hardcoding these capabilities, your assistant discovers them dynamically.

Invoking an MCP Tool

Suppose the assistant needs to search for a file in the project.

var result = await client.CallToolAsync(
    "searchFiles",
    new
    {
        pattern = "*.cs"
    });

Console.WriteLine(result);

The AI model decides when to invoke the tool, while the MCP server performs the actual operation and returns the result.

Production Considerations

Dependency Injection

This avoids creating multiple client instances and centralizes configuration, making the application easier to maintain and test.

Configuration

Store server URLs, model names, and authentication settings in appsettings.json.

{
  "Mcp": {
    "ServerUrl": "http://localhost:3000"
  }
}

Keep API keys and secrets in environment variables or Azure Key Vault instead of storing them in source code.

Logging

Monitor interactions between your application and the MCP server by logging:

Connection attempts
Tool execution
Execution time
Errors
Retry attempts

Avoid logging source code, credentials, or confidential project information.

Error Handling

External tools may fail due to network issues, unavailable services, or invalid requests.

Implement proper exception handling to:

Detect connection failures.
Handle unavailable tools.
Retry transient errors.
Return user-friendly error messages.

Your assistant should fail gracefully instead of terminating unexpectedly.

Security

An AI coding assistant may access sensitive project resources, making security a top priority.

Follow these practices:

Authenticate every MCP client.
Authorize access to individual tools.
Validate user input before tool execution.
Restrict access to production resources.
Use HTTPS for all communications.
Store credentials securely.

Never allow unrestricted execution of tools that can modify files or execute system commands.

Performance

AI coding assistants often make multiple tool requests during a single conversation.

Improve performance by:

Reusing MCP client connections.
Caching frequently accessed metadata.
Executing independent tool calls asynchronously.
Reducing unnecessary tool discovery requests.
Monitoring response latency.

Efficient communication improves both responsiveness and scalability.

Extending to Multi-Agent Systems

As the assistant grows, different AI agents can specialize in specific development tasks.

Examples include:

Code Generation Agent
Documentation Agent
Testing Agent
Security Review Agent
Refactoring Agent
Repository Analysis Agent

Each agent interacts with the same MCP server while focusing on a specific responsibility, resulting in cleaner workflows and better code quality.

Deployment

Deploy the ASP.NET Core application using your preferred hosting platform.

Common deployment options include:

Azure App Service
Azure Container Apps
Docker
Kubernetes

Ensure the MCP server is accessible from the deployed application and configure environment-specific settings securely.

Best Practices

Design MCP tools with a single responsibility.
Keep prompts specific and task-oriented.
Validate all tool inputs.
Cache frequently used data when appropriate.
Secure every external integration.
Monitor latency and tool usage.
Regularly review permissions granted to AI agents.

Common Mistakes

Avoid these common pitfalls when building AI coding assistants:

Hardcoding MCP server URLs.
Exposing unrestricted file system access.
Ignoring authentication and authorization.
Logging sensitive project information.
Calling every available tool unnecessarily.
Trusting AI-generated code without review.

Keeping integrations secure and focused improves both reliability and maintainability.

Troubleshooting

Problem	Solution
Unable to connect to the MCP server	Verify the server URL, network connectivity, and server availability.
No tools are discovered	Ensure the MCP server is running and exposes registered tools.
Tool execution fails	Validate tool parameters and review server logs.
Authentication errors	Check API keys, access tokens, and authentication configuration.
Slow responses	Cache metadata, optimize tool implementations, and reduce unnecessary requests.

Conclusion

Building an AI coding assistant with ASP.NET Core and MCP servers enables your application to go beyond simple text generation by interacting with real development resources such as source code, documentation, APIs, and repositories. By combining ASP.NET Core, Large Language Models, and the Model Context Protocol, developers can create intelligent assistants that automate development workflows while remaining modular and extensible.

As your application evolves, you can expand from a single AI assistant to a multi-agent architecture, where specialized agents collaborate through MCP to deliver more accurate, secure, and scalable development experiences.