Sharepointmcp

🚀 .NET MCP for SharePoint: MCP Server Using Graph API and Semantic Kernel

In a modern enterprise environment, it's crucial to access specific data securely and efficiently, especially when integrating with artificial intelligence capabilities. Imagine a chatbot that can not only have intelligent conversations but also directly retrieve relevant documents or information from an organization's SharePoint sites. This article outlines a modular architecture using .NET, Microsoft Graph API, Semantic Kernel, and Gemini AI to achieve this goal.

The project consists of three distinct modules:

1. Semantic Kernel MCP Server: The core component that interacts with SharePoint.
2. SharePoint Connector Library: The "hands" for in - depth access to SharePoint Online.
3. MCP Client for Gemini AI: The user - facing chat interface.

Let's break down each module step by step:

🚀 Quick Start

Module 1: .NET MCP Server (Using Semantic Kernel)

This core component serves as the central hub and API endpoint. It is built using .NET, with Semantic Kernel integrated. While it could be a standard ASP.NET Core Web API, using Semantic Kernel here can potentially define SharePoint interactions as a "skill" or "plugin".

It exposes functionality (e.g., SharePoint search) through API endpoints (e.g., /api/sharepoint/search), which accept search queries. It coordinates calls to functions in the connector library, using .NET (e.g., ASP.NET Core Web API) and the Semantic Kernel SDK.

Key Features: Define an API route (e.g., /api/sharepoint/search) that accepts search queries. This route will call functions in the connector library. Thanks to the use of Semantic Kernel, it can be easily integrated into more complex AI inference processes if needed.

Module 2: SharePoint Connector Library (Graph API)

This .NET library is specifically responsible for the details of communicating with SharePoint Online.

Purpose: Authenticate using the Microsoft Graph API and perform specific SharePoint operations, starting with search. Technology: .NET Standard or .NET Core library, Microsoft.Graph SDK. Authentication: Use a client ID and client secret (app - only authentication). Setup: You need to register an application in Azure Active Directory (Azure AD), grant it the necessary Graph API permissions (e.g., Sites.Read.All or more granular permissions depending on your needs), and obtain administrator consent. It's crucial to store your client secret securely (e.g., Azure Key Vault, .NET user secrets) - never hard - code it!

Key Features: An asynchronous function (e.g., SearchSharePointAsync(string query)) that:

• Obtains an access token for Microsoft Graph using the client ID and secret.
• Builds a Graph API request to the SharePoint search endpoint (/search/query).
• Sends the request using the Microsoft.Graph SDK.
• Parses the response and returns relevant search results (e.g., document names, links, excerpts).

Module 3: MCP Client (Gemini AI)

This is the application for user interaction. It has a chat interface powered by Gemini AI.

Purpose: Provide an AI - based conversational experience. Identify user intents related to SharePoint and delegate tasks to the MCP server. Technology: Any client platform (.NET MAUI, Blazor, console application, etc.), Google AI Gemini SDK.

💻 Usage Examples

Basic Usage

public async Task Search(string query)
{
    var response = await _httpClient.GetAsync($"https://api.yourbackend.com/search?q={query}");
    var result = await response.Content.ReadAsStringAsync();
    // Process the result and display it to the user
}

✨ Features

• Modular Architecture: Clearly separates front - end and back - end logic, facilitating maintenance and expansion.
• Scalability: New search functions can be easily added or other services can be integrated.
• High Performance: The efficient performance of .NET and Gemini AI ensures fast responses.

With this modular architecture, you can build an efficient and easily manageable SharePoint search solution.