mcp-dotnet-samples

Overview

This repository provides a comprehensive set of samples demonstrating how to create and use MCP servers in .NET applications. It includes several working implementations like a GitHub Copilot customization file retriever, Markdown to HTML converter, Outlook email sender, and to-do list manager. The samples showcase how to connect AI models to different data sources and tools using Microsoft's .NET framework, with Docker support for easy deployment across different IDEs including VS Code and Visual Studio.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

• get_copilot_files

  Retrieves GitHub Copilot customization files from awesome-copilot repository

  Try:Show me the latest Copilot customization filesTry:Find GitHub Copilot configurations for C#
• convert_markdown_to_html

  Converts markdown text to HTML format

  Try:Convert this markdown to HTMLTry:Transform README.md into HTML
• send_email

  Sends emails through Outlook

  Try:Send an email to my teamTry:Email the project status update
• add_todo

  Adds a new item to the to-do list

  Try:Add 'review pull requests' to my to-do listTry:Create a new task: finish documentation
• list_todos

  Lists all items in the to-do list

  Try:Show me my to-do listTry:What tasks do I have pending?
• complete_todo

  Marks a to-do item as complete

  Try:Mark 'finish report' as completeTry:Check off the meeting task

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Installation

docker run -i --rm ghcr.io/microsoft/mcp-dotnet-samples/[sample-name]:latest

{
  "mcpServers": {
    "[sample-name]": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/microsoft/mcp-dotnet-samples/[sample-name]:latest"]
    }
  }
}

FAQ

What is the Model Context Protocol (MCP)?

MCP is an open protocol that standardizes how applications provide context to Large Language Models (LLMs), acting like a standardized connector between AI models and data sources.

Can I use these samples in production environments?

These samples are designed for demonstration and learning purposes. While they provide working implementations, additional security and error handling should be added for production use.

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