Azure AI Search
Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.
What is Azure AI Search?
Azure AI Search is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to enables claude to search and retrieve documents from azure ai search indexes with intelligent summarization and analysis using langgraph workflows and optional google gemini integration.
Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Enables Claude to search and retrieve documents from Azure A
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx azure-ai-search-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Azure AI Search
The Azure AI Search MCP Server connects Claude and other MCP clients to Azure AI Search indexes, enabling keyword, vector, and hybrid document retrieval directly from AI conversations. Built with Python and using either direct Azure AI Search integration or Azure AI Agent Service with LangGraph workflows, it also supports optional Bing web grounding for broader search coverage. Teams managing large document repositories in Azure use it to give Claude read access to their indexed knowledge bases without exposing raw database credentials.
Prerequisites
- Python 3.10 or later and the uv package manager installed
- An active Azure subscription with an Azure AI Search resource and at least one configured index
- Azure credentials: either an API key or service principal with Search Index Data Reader role
- Optional: Azure AI Projects connection string if using the Agent Service integration with Gemini/Bing grounding
- An MCP-compatible client such as Claude Desktop
Clone the repository and set up a virtual environment
Clone the project and create an isolated Python environment using uv.
git clone https://github.com/farzad528/mcp-server-azure-ai-agents.git
cd mcp-server-azure-ai-agents
uv venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall dependencies for your chosen integration
Install the direct Azure AI Search dependencies. If you need the Agent Service variant with LangGraph, also install azure-ai-projects.
uv pip install "mcp[cli]" azure-search-documents==11.5.2 azure-identity python-dotenvCreate a .env file with your Azure credentials
Create a .env file in the project root with your Azure AI Search connection details. Use the direct search variables for the standalone integration.
AZURE_SEARCH_SERVICE_ENDPOINT=https://your-service.search.windows.net
AZURE_SEARCH_INDEX_NAME=your-index-name
AZURE_SEARCH_API_KEY=your-api-keyTest the server locally
Run the server using the MCP CLI inspector to verify connectivity before wiring it into Claude Desktop.
mcp dev server.pyAdd the server to Claude Desktop configuration
Edit claude_desktop_config.json to point Claude Desktop at your local server installation. Use the absolute path to your virtual environment's Python executable.
Restart Claude Desktop and test a search query
After restarting, open a conversation and ask Claude to search your index. Use the hammer icon in the input field to select the Azure AI Search tool if it does not trigger automatically.
Azure AI Search Examples
Client configuration
Add this to claude_desktop_config.json, adjusting the directory path to your cloned repository.
{
"mcpServers": {
"azure-ai-search": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/mcp-server-azure-ai-agents",
"run",
"server.py"
],
"env": {
"AZURE_SEARCH_SERVICE_ENDPOINT": "https://your-service.search.windows.net",
"AZURE_SEARCH_INDEX_NAME": "your-index-name",
"AZURE_SEARCH_API_KEY": "your-api-key"
}
}
}
}Prompts to try
Once connected, use these prompts to search your Azure AI Search index.
- "Search my Azure index for documents about neural networks using hybrid search"
- "Find all policy documents mentioning GDPR compliance in my search index"
- "Use vector search to find content semantically similar to 'customer onboarding best practices'"
- "Keyword search my index for 'quarterly earnings report 2024'"Troubleshooting Azure AI Search
Authentication error when connecting to Azure AI Search
Verify your AZURE_SEARCH_API_KEY is correct and the key has at least the Query key permission. Alternatively, use DefaultAzureCredential by omitting the key and ensuring your account has the Search Index Data Reader role.
azure-search-documents version conflict
The project requires exactly version 11.5.2. Install with `uv pip install azure-search-documents==11.5.2` to avoid conflicts with newer versions that changed the API.
Vector search returns no results
Vector search requires the index to have a vector field with an embedding model configured. Confirm your index schema includes a vector field and that embeddings have been populated for your documents.
Frequently Asked Questions about Azure AI Search
What is Azure AI Search?
Azure AI Search is a Model Context Protocol (MCP) server that enables claude to search and retrieve documents from azure ai search indexes with intelligent summarization and analysis using langgraph workflows and optional google gemini integration. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Azure AI Search?
Follow the installation instructions on the Azure AI Search GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Azure AI Search?
Azure AI Search works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Azure AI Search free to use?
Yes, Azure AI Search is open source and available under the NOASSERTION license. You can use it freely in both personal and commercial projects.
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