Azure AI Agent Service + Azure AI Search MCP Server

A Model Context Protocol (MCP) server that enables Claude Desktop to search your content using Azure AI services. Choose between Azure AI Agent Service (with both document search and web search) or direct Azure AI Search integration.

Overview

This project provides two MCP server implementations to connect Claude Desktop with Azure search capabilities:

1. Azure AI Agent Service Implementation (Recommended) - Uses the powerful Azure AI Agent Service to provide:

   • Azure AI Search Tool - Search your indexed documents with AI-enhanced results
   • Bing Web Grounding Tool - Search the web with source citations

2. Direct Azure AI Search Implementation - Connects directly to Azure AI Search with three methods:

   • Keyword Search - Exact lexical matches
   • Vector Search - Semantic similarity using embeddings
   • Hybrid Search - Combination of keyword and vector searches

Features

• AI-Enhanced Search - Azure AI Agent Service optimizes search results with intelligent processing
• Multiple Data Sources - Search both your private documents and the public web
• Source Citations - Web search results include citations to original sources
• Flexible Implementation - Choose between Azure AI Agent Service or direct Azure AI Search integration
• Seamless Claude Integration - All search capabilities accessible through Claude Desktop's interface
• Customizable - Easy to extend or modify search behavior

Quick Links

• Get Started with Azure AI Search
• Azure AI Agent Service Quickstart

Requirements

• Python: Version 3.10 or higher
• Claude Desktop: Latest version
• Azure Resources:
  • Azure AI Search service with an index containing vectorized text data
  • For Agent Service: Azure AI Project with Azure AI Search and Bing connections
• Operating System: Windows or macOS (instructions provided for Windows, but adaptable)

Azure AI Agent Service Implementation (Recommended)

Setup Guide

1. Project Directory:

   mkdir mcp-server-azure-ai-search
   cd mcp-server-azure-ai-search
   

2. Create a .env File:

   echo "PROJECT_CONNECTION_STRING=your-project-connection-string" > .env
   echo "MODEL_DEPLOYMENT_NAME=your-model-deployment-name" >> .env
   echo "AI_SEARCH_CONNECTION_NAME=your-search-connection-name" >> .env
   echo "BING_CONNECTION_NAME=your-bing-connection-name" >> .env
   echo "AI_SEARCH_INDEX_NAME=your-index-name" >> .env
   

3. Set Up Virtual Environment:

   uv venv
   .venv\Scripts\activate
   uv pip install "mcp[cli]" azure-identity python-dotenv azure-ai-projects
   

4. Use the azure_ai_agent_service_server.py script for integration with Azure AI Agent Service.

Azure AI Agent Service Setup

Before using the implementation, you need to:

1. Create an Azure AI Project:

   • Go to the Azure Portal and create a new Azure AI Project
   • Note the project connection string and model deployment name

2. Create an Azure AI Search Connection:

   • In your Azure AI Project, add a connection to your Azure AI Search service
   • Note the connection name and index name

3. Create a Bing Web Search Connection:

   • In your Azure AI Project, add a connection to Bing Search service
   • Note the connection name

4. Authenticate with Azure:

   az login
   

Configuring Claude Desktop

{
  "mcpServers": {
    "azure-ai-agent": {
      "command": "C:\\path\\to\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\azure_ai_agent_service_server.py"],
      "env": {
        "PROJECT_CONNECTION_STRING": "your-project-connection-string",
        "MODEL_DEPLOYMENT_NAME": "your-model-deplo

... [View full README on GitHub](https://github.com/farzad528/mcp-server-azure-ai-agents#readme)