A collection of official Microsoft MCP servers, providing AI assistant integration tools for various services such as Azure, GitHub, Microsoft 365, and Fabric. It supports local and remote deployment, helping developers connect AI models with various data sources and tools through a standardized protocol.

The Azure MCP Server is a server that implements the MCP protocol, providing seamless connection between AI agents and Azure services (such as Storage and Cosmos DB), and supporting the conversion of natural language commands into Azure operations.

An implementation of the MCP Server for Azure DevOps, providing a standardized protocol for AI assistants to access the Azure DevOps API

This project provides a set of tools and services to integrate Power Platform CLI, Graph API, and Azure REST API through the Model Context Protocol (MCP), support interaction with AI models, and simplify the development process.

The ROADrecon MCP Server provides Azure AD data security analysis services for AI assistants

This project is a server that implements the MCP protocol, providing AI assistants with the ability to interact with Azure DevOps services and enabling functions such as natural language operations on work items, project management, and team collaboration.

This project demonstrates how to integrate an AI agent powered by Azure OpenAI with the Microsoft Fabric data warehouse through the Model Context Protocol (MCP), using GraphQL as a common data connection layer to achieve bidirectional access to enterprise data.

This is a project demonstrating how to use MCP Servers in a Chainlit environment. It can be quickly deployed through Azure Open AI integration and Dev Containers, supporting examples such as file system operations.

This project is an Azure troubleshooting server based on the Model Context Protocol (MCP), which allows AI assistants to interactively diagnose Azure resource problems through the Azure self-service API.

This project shows how to create an MCP server and client for Azure Cosmos DB, including a front - end application (product catalog and AI assistant) and an MCP server component that connects to the database.

This project is a server based on the MCP protocol, providing AI assistants with the ability to interact with Azure DevOps services, enabling natural - language operations on work items, project management, and other functions.

Azure Database for PostgreSQL MCP Server is a server based on the Model Context Protocol (MCP) standard that allows AI models to interact with Azure PostgreSQL databases and provides standardized data access capabilities.

Snippy is an intelligent code snippet management service based on Azure Functions. It uses the MCP protocol to interact with tools like Copilot, supporting functions such as code snippet storage, search, and AI analysis.

This project provides two MCP server implementations that connect Claude Desktop to Azure search capabilities, supporting searches for document and web content through the Azure AI Agent Service (recommended) or direct Azure AI Search integration.

The Azure CLI MCP Server is a server based on the MCP protocol. It enhances the functions of Azure CLI by wrapping it and providing friendly prompts, and supports the execution of Azure CLI commands driven by AI.

A service that interacts with Microsoft To Do tasks through an AI assistant, requiring a Node.js environment and Azure application registration for authentication configuration.

Azure MCP Hub is a platform that helps developers quickly build, run, or reuse Model Context Protocol (MCP) servers. It supports multiple programming languages and AI frameworks, enabling AI agents to call real APIs.

MCP Server DevOps Bridge is a bridge project connecting DevOps tools and AI assistants, providing a unified natural language interface, supporting cross - platform integration and autonomous agent workflows, including the integration of platforms such as Azure DevOps, GitHub, and Slack, as well as an intelligent memory system.

Hermes Search MCP Server is an open - source service based on the Model Context Protocol, providing full - text and semantic search functions integrated with Azure Cognitive Search, and supporting multiple AI client tools.

OmniLLM is an MCP server that serves as a bridge between Claude and other large language models (such as ChatGPT, Azure OpenAI, and Google Gemini), providing a unified AI access interface.

Use Azure API Management as the AI gateway for MCP servers to achieve secure access to remote MCP servers and support the latest MCP authorization specification.

This project implements an MCP service based on Azure Functions, serving as a bridge between AI agents and the AlphaVantage financial API, providing access to financial data such as company overviews and financial statements.

An MCP server integrating Azure AI Search Agent Service with Claude Desktop

Use Azure API Management as an AI gateway to achieve secure access to remote MCP servers through Azure Functions, following the latest MCP authorization specification.

An MCP server that connects to Azure Kusto, providing data schema exploration and KQL query execution functions for AI assistants, supporting VS Code integration and data analysis assistance.

This project provides two implementations of the MCP server, connecting Claude Desktop with Azure search capabilities, supporting document search and web search. You can choose to use the Azure AI Agent service or directly integrate Azure AI Search.

The distributed MCP server provides an API toolset for legal information query, weather data, Azure pricing, and utility functions, supporting AI assistants such as Microsoft Copilot to call through the MCP protocol.

The Azure AI Agent Service MCP Server integrates the functions of Azure AI Foundry, supports connecting to existing Azure AI agents, and provides capabilities such as security authentication, session memory, and multi-tool invocation.

This project showcases multiple Model Context Protocol (MCP) servers integrated with Microsoft Azure, which enable large language models (LLMs) to securely access Azure tools and data sources, including AI agent services and various database services.

This project provides a series of KQL queries to detect abuse behaviors related to the BloodHound BARK toolkit in Azure AD tenants. These queries aim to identify behaviors simulated by BARK rather than the use of the tool itself. The content includes detection queries for different BARK functions, control suggestions, and relevant resource links.

This project aims to evaluate the performance and development efficiency of the MCP server based on Azure PostgreSQL in clinical data analysis. By comparing with direct SQL queries, it verifies whether the MCP abstraction layer can reduce development complexity while maintaining or improving operational efficiency.

This is a generative AI course project for beginners, providing a complete learning path from environment setup to practical application. The project includes 6 concept courses and 6 programming courses, using the Azure OpenAI service for practical operations. Learners can quickly start through GitHub Codespaces or choose to install the Python environment locally. The project emphasizes the secure management of API keys and provides detailed setup guides. In addition, learners are encouraged to participate in open-source contributions, and an AI community communication platform is provided.

This project is a remote Model Context Protocol (MCP) server based on Node.js and TypeScript, deployed in Azure Container Apps, used to implement standardized communication between AI models and tools. It provides a quick start guide, supports local development, GitHub Codespaces, and Azure deployment, and includes SSE protocol implementation, SQLite database integration, and tool interaction features.

Azure AI Vision Face Liveness Detection MCP Server for embedding proof of existence in Agentic AI workflows.

An Azure DevOps server that implements the Model Context Protocol (MCP), providing a standardized interface for AI assistants to securely access and manage Azure DevOps resources.

An MCP protocol memory and knowledge graph server implemented based on .NET 10 Azure Functions, providing persistent project memory storage for AI assistants, supporting workspace isolation and entity relationship management.

The Azure Policy MCP Server project aims to help LLM generate, validate, and deploy custom Azure policies by providing a toolset, including policy discovery, structural validation, deployment management, and compliance query functions.

An MCP server implementation for Azure DevOps that provides a standardized protocol for AI assistants to access the Azure DevOps API

This project provides two implementations of the MCP server, connecting Claude Desktop with Azure search capabilities and supporting document and web content search through Azure AI agent service (recommended) or direct Azure AI Search integration.

The MCP server for Azure Data Explorer provides a standardized interface for AI assistants to execute KQL queries and explore data. It supports database resource discovery, query execution, multiple authentication methods, and Docker containerization.