Azure AI Search Integration for Claude

Overview

This project provides two Model Context Protocol (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:
2. Azure AI Search Tool - Search your indexed documents with AI-enhanced results
3. Bing Web Grounding Tool - Search the web with source citations
4. Direct Azure AI Search Implementation - Connects directly to Azure AI Search with three methods:
5. Keyword Search - Exact lexical matches
6. Vector Search - Semantic similarity using embeddings
7. 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:
   shell mkdir mcp-server-azure-ai-search cd mcp-server-azure-ai-search

2. Create a .env File:
   shell 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:
   shell 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:
2. Go to the Azure Portal and create a new Azure AI Project
3. Note the project connection string and model deployment name
4. Create an Azure AI Search Connection:
5. In your Azure AI Project, add a connection to your Azure AI Search service
6. Note the connection name and index name
7. Create a Bing Web Search Connection:
8. In your Azure AI Project, add a connection to Bing Search service
9. Note the connection name
10. Authenticate with Azure:
    shell 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-deployment-name",
        "AI_SEARCH_CONNECTION_NAME": "your-search-connection-name",
        "BING_CONNECTION_NAME": "your-bing-connection-name",
        "AI_SEARCH_INDEX_NAME": "your-index-name"
      }
    }
  }
}

Note: Replace path placeholders with your actual project paths.

Direct Azure AI Search Implementation

For those who prefer direct Azure AI Search integration without the Agent Service:

1. Create a different .env File:
   shell echo "AZURE_SEARCH_SERVICE_ENDPOINT=https://your-service-name.search.windows.net" > .env echo "AZURE_SEARCH_INDEX_NAME=your-index-name" >> .env echo "AZURE_SEARCH_API_KEY=your-api-key" >> .env

2. Install Dependencies:
   shell uv pip install "mcp[cli]" azure-search-documents==11.5.2 azure-identity python-dotenv

3. Use the azure_search_server.py script for direct integration with Azure AI Search.

4. Configure Claude Desktop:
   json { "mcpServers": { "azure-search": { "command": "C:\\path\\to\\.venv\\Scripts\\python.exe", "args": ["C:\\path\\to\\azure_search_server.py"], "env": { "AZURE_SEARCH_SERVICE_ENDPOINT": "https://your-service-name.search.windows.net", "AZURE_SEARCH_INDEX_NAME": "your-index-name", "AZURE_SEARCH_API_KEY": "your-api-key" } } } }

Testing the Server

1. Restart Claude Desktop to load the new configuration
2. Look for the MCP tools icon (hammer icon) in the bottom-right of the input field
3. Try queries such as:
4. "Search for information about AI in my Azure Search index"
5. "Search the web for the latest developments in LLMs"
6. "Find information about neural networks using hybrid search"

Troubleshooting

• Server Not Appearing:
• Check Claude Desktop logs (located at %APPDATA%\Claude\logs\mcp*.log on Windows)
• Verify file paths and environment variables in the configuration
• Test running the server directly: python azure_ai_agent_service_server.py or uv run python azure_ai_agent_service_server.py
• Azure AI Agent Service Issues:
• Ensure your Azure AI Project is correctly configured
• Verify that connections exist and are properly configured
• Check your Azure authentication status

Customizing Your Server

• Modify Tool Instructions: Adjust the instructions provided to each agent to change how they process queries
• Add New Tools: Use the @mcp.tool() decorator to integrate additional tools
• Customize Response Formatting: Edit how responses are formatted and returned to Claude Desktop
• Adjust Web Search Parameters: Modify the web search tool to focus on specific domains

License

This project is licensed under the MIT License.

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Model Context Protocol Servers for Azure AI Search

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