Azure AI Search Server

An MCP (Model Context Protocol) server that exposes Azure AI Search functionality as tools for AI assistants.

Features

• Full-text search - Search documents using Azure AI Search
• Semantic/vector search - Perform semantic search with reranking
• Index management - List indexes and get schema information
• Document retrieval - Get documents by key or count documents

Available Tools

Tool	Description
search	Full-text search with filters and field selection
vector_search	Semantic search with reranking scores
list_indexes	List all available search indexes
get_index_schema	Get fields and schema of an index
get_document	Retrieve a specific document by key
get_document_count	Count documents in an index

Setup

Prerequisites

• Python 3.10+
• Azure AI Search service
• Azure Search API key or Azure credentials

Installation

1. Clone the repository:

   git clone <repository-url>
   cd aisearch-mcp
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure environment variables:

   cp .env.example .env
   # Edit .env with your Azure Search credentials
   

Configuration

Set the following environment variables in your .env file:

Variable	Description	Required
AZURE_SEARCH_ENDPOINT	Azure Search service URL (e.g., https://mysearch.search.windows.net)	Yes
AZURE_SEARCH_API_KEY	Azure Search admin or query key	Yes*
AZURE_SEARCH_INDEX	Default search index name	Yes
MCP_PORT	Server port (default: 9000)	No

*If not provided, the server will use DefaultAzureCredential for authentication.

Running the Server

Local

python server.py

The server will start on http://0.0.0.0:9000 with the following endpoints:

• SSE Transport: GET /sse (establish connection), POST /messages (send messages)
• Streamable HTTP: POST /mcp

Docker

Build and run with Docker:

# Build the image
docker build -t azure-search-mcp .

# Run with environment variables
docker run -p 9000:9000 \
  -e AZURE_SEARCH_ENDPOINT=https://your-search.search.windows.net \
  -e AZURE_SEARCH_API_KEY=your-api-key \
  -e AZURE_SEARCH_INDEX=your-index \
  azure-search-mcp

# Or run with .env file
docker run -p 9000:9000 --env-file .env azure-search-mcp

Connecting MCP Clients

VS Code / Claude Desktop (SSE)

Add to your MCP configuration:

{
  "mcpServers": {
    "azure-search": {
      "url": "http://localhost:9000/sse"
    }
  }
}

Streamable HTTP Clients

{
  "mcpServers": {
    "azure-search": {
      "url": "http://localhost:9000/mcp"
    }
  }
}

Stdio (Local Process)

For clients that support stdio transport, run directly:

{
  "mcpServers": {
    "azure-search": {
      "command": "python",
      "args": ["/path/to/server.py"]
    }
  }
}

Example Usage

Once connected, you can use the tools through your MCP client:

• Search for hotels: "Search for hotels with pool in Seattle"
• Get index schema: "What fields are in the hotels-sample-index?"
• Count documents: "How many documents are in the index?"

License

MIT