# 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…
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# 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.  --- ##…
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Author
vuutla
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- AI-enhanced search using Azure AI Agent Service
- Search both private indexed documents and the public web
- Web search results include citations to original sources
- Choose between Azure AI Agent Service or direct Azure AI Search
- Seamless integration with Claude Desktop’s interface
- Customizable tools, instructions, and search parameters
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin NameAzure AI Agent Service + Azure AI Search MCP Server
Command (node, npx, python, etc.)
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches
Setting up with Cursor
Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
Setting up with Claude Desktop
Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system
Note: Make sure Claude Desktop is running before attempting to connect.
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
Setting up with Windsurf
Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
Setting up with Cline
Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system
Note: Make sure to restart Cline after adding the configuration.
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
Setting up with Goose
Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
JSON
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
From the repository
Install Python 3.10+, set up a virtual environment with uv, install dependencies (mcp[cli], azure-identity, python-dotenv, etc.), and configure Claude Desktop by adding a server entry pointing to either azure_ai_agent_service_server.py or azure_search_server.py with the required environment variables. Authenticate with Azure using az login and restart Claude Desktop.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"azure ai agent service + azure ai search mcp server": {
"mcp-server-azure-ai-agents-vuutla": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}
# 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](https://learn.microsoft.com/en-us/azure/search/search-get-started-portal)
- [Azure AI Agent Service Quickstart](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/agent-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:**
```bash
mkdir mcp-server-azure-ai-search
cd mcp-server-azure-ai-search
```
2. **Create a `.env` File:**
```bash
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:**
```bash
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:**
```bash
az login
```
### Configuring Claude Desktop
```json
{
"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:**
```bash
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:**
```bash
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:
- "Search for information about AI in my Azure Search index"
- "Search the web for the latest developments in LLMs"
- "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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