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 Microsoft official MCP server provides search and access functions for the latest Microsoft technical documentation for AI assistants

An MCP server for integrating Microsoft Bing Search API, supporting web page, news, and image search functions, providing network search capabilities for AI assistants.

Office-Word-MCP-Server is a server based on the Model Context Protocol that provides a standard interface for AI assistants to create, read, and operate Microsoft Word documents, supporting rich document editing functions.

A Word document operation server based on the MCP protocol, providing AI assistants with the ability to create, edit, and manage Microsoft Word documents.

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.

Edit-MCP is a model context protocol server integrated with the Microsoft Edit tool, providing advanced file editing capabilities for AI systems and adopting a hybrid architecture that combines direct file operations and Edit integration.

This experiment guides users on how to deploy an MCP server and integrate it into Microsoft Copilot Studio. Through a standardized protocol, it connects AI models with data sources to achieve real - time data access and joke generation functions.

MS SQL MCP Server is a no-code bridge tool that allows AI assistants to directly query and explore Microsoft SQL Server databases. It supports database discovery, table structure viewing, secure execution of read-only SQL queries, and the generation of SQL from natural language.

A service for interacting with Microsoft To Do tasks via an AI assistant

Microsoft MCP Server for Enterprise is a publicly previewed server based on the Model Context Protocol that allows AI agents to access Microsoft Entra data through natural language queries and convert them into Microsoft Graph API calls, aiming to help developers and IT administrators integrate organizational data management into AI workflows.

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.

The AutoGen MCP server is a multi - agent dialogue service integrating the Microsoft AutoGen framework, providing standardized interfaces for AI agent management and collaboration.

mcp - searxng is an MCP server example designed for AI agents, which implements the external information search function through the open - source meta - search engine SearXNG. The project demonstrates how to integrate SearXNG and Microsoft's markdownify tool to convert web page content into Markdown - formatted text and communicate with MCP clients via the SSE protocol.

An MCP server integrated with the Microsoft Graph API, providing features such as Teams team collaboration, user management, and message search, and supporting AI assistants to interact with the Microsoft ecosystem through the protocol.

A Model Context Protocol (MCP) server implemented for Microsoft OneNote, providing a standardized interface for AI language models to interact with OneNote and supporting management functions for notebooks, sections, and pages.

UpTier is a desktop to - do application that combines the Microsoft To Do - style interface with the intelligent priority sorting function of Claude AI, enabling intelligent task management through the MCP protocol.

An MCP server implementation based on Spring Boot and Spring AI, enabling AI agents to search and navigate files and folders in Microsoft OneDrive.

An MCP server based on Node.js and TypeScript for AI agents to interact with Microsoft SQL Server databases, providing table structure query and CRUD operation functions.

This project demonstrates how to integrate Model Context Protocol (MCP) tools with Microsoft Semantic Kernel to achieve seamless interaction between AI models and external data sources or tools. By standardizing the interaction between applications and AI models through the MCP protocol and combining the powerful functions of Semantic Kernel, developers can expand AI capabilities, dynamically call external functions, and simplify the orchestration of complex workflows.