Overview

The MCP .NET Samples repository is a curated set of reference implementations that illustrate how the Model Context Protocol (MCP) can be integrated into .NET applications. MCP is a lightweight, vendor‑agnostic protocol that standardizes the way large language models (LLMs) receive context, invoke external tools, and orchestrate complex workflows. By exposing a uniform API surface, MCP allows developers to plug in diverse data sources—GitHub repositories, email clients, or custom converters—without having to write bespoke adapters for each LLM provider.

Problem Solved

Modern AI assistants often need to access proprietary or specialized data, perform transformations, or interact with third‑party services. Without a common interface, each integration requires custom code, leading to fragmented solutions that are hard to maintain and scale. MCP solves this by defining a clear contract between the AI model and external services, enabling rapid iteration and consistent security practices across environments. Developers can focus on business logic rather than protocol gymnastics, ensuring that sensitive data stays within the organization’s infrastructure.

What the Server Does

Each sample in the repository implements an MCP server that exposes a specific capability:

• Awesome Copilot retrieves GitHub Copilot configuration files, allowing an LLM to adapt its suggestions based on a project’s unique coding style.
• Markdown to HTML converts markdown documents into well‑structured HTML, enabling AI assistants to generate web‑ready content on the fly.
• Outlook Email integrates with Microsoft Outlook, letting an LLM read, compose, and manage emails through a secure, standard interface.

These servers expose resources (data sets), tools (operations that can be invoked by the model), and prompts (pre‑defined instruction templates). By adhering to MCP, they can be consumed by any compliant AI client—whether it’s a custom chat interface or an automated agent—without modification.

Key Features and Capabilities

• Resource Discovery: Clients can query available datasets or configuration files before making calls, ensuring that the model only accesses what it needs.
• Tool Invocation: The protocol supports synchronous and asynchronous tool calls, allowing LLMs to perform tasks such as file conversion or email composition.
• Secure Data Handling: All interactions are sandboxed, and sensitive data can be protected with existing .NET security mechanisms (e.g., Azure Key Vault integration).
• Provider Agnosticism: Because MCP is independent of the underlying LLM, developers can switch between providers (Azure OpenAI, Anthropic, etc.) without changing the integration code.

Real‑World Use Cases

• Code Generation and Review: An AI assistant can pull project‑specific guidelines from GitHub Copilot configurations, ensuring that generated code aligns with team conventions.
• Content Creation Pipelines: Markdown documents authored in a CMS can be automatically rendered to HTML for web publishing, with the LLM handling stylistic adjustments on demand.
• Enterprise Email Automation: Agents can draft, schedule, and reply to emails using Outlook’s API while keeping all interactions within the corporate network.

Integration with AI Workflows

These MCP servers can be dropped into any LLM‑driven workflow. For example, a conversational agent might first query the Markdown to HTML server to format user‑submitted notes before presenting them in a chat. Because MCP defines the contract, the same server can be reused across different assistants—whether they run on Azure OpenAI, AWS Bedrock, or a self‑hosted model—without any code changes. This plug‑and‑play nature accelerates development, reduces duplication of effort, and ensures consistent behavior across environments.

In summary, the MCP .NET Samples repository demonstrates how a standardized protocol can unlock powerful, secure, and reusable integrations for AI assistants. By providing ready‑to‑use servers that cover common tasks—code customization, content transformation, and email handling—developers gain a solid foundation for building sophisticated, data‑aware AI applications.