No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by microsoft · MCP Server · ★ 196
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Model Context Protocol .NET Samples 🚀 Introduction Welcome to the Model Context Protocol (MCP) .NET Samples repository! This collection of samples demonstrates how to leverage the Model Context Protocol in .NET applications. MCP is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools. With MCP, you can: Utilize pre-built integrations that your LLM can directly plug into Flexibly switch between LLM providers and vendors Follow best practices for securing your data within your infrastructure Build agents and complex workflows on top of LLMs This repository contains .NET samples ranging from building your own MCP implementation to integrating with Azure services. 📋 Sample Projects
| Stars | 196 |
| Forks | 64 |
| Language | C# |
| Category | MCP Server |
| License | MIT |
| Quality Score | 62.6243274107197/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-22 |
| Created | 2025-04-14 |
| Platforms | cli, dotnet, mcp |
| Est. Tokens | ~19k |
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mcp-dotnet-samples is A comprehensive set of samples of creating and using MCP servers and clients with .NET. It is categorized as a MCP Server with 196 GitHub stars.
mcp-dotnet-samples is primarily written in C#. It covers topics such as dotnet, mcp, mcp-client.
You can find installation instructions and usage details in the mcp-dotnet-samples GitHub repository at github.com/microsoft/mcp-dotnet-samples. The project has 196 stars and 64 forks, indicating an active community.
mcp-dotnet-samples is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-dotnet-samples on Agent Skills Hub include MCPSharp, AgentChat, flow-like. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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