Experimental MCP Service for Azure AI Search

This experimental MCP service provides you with the following capabilities:

• Retrieve a List of AI Search Indices from your Service

• Get Details about the Schema and Configuration of a Specific AI Search Service

• Create new Indices

• Update Existing Indices

• Run Queries against specific AI Search Indices

• Create and Update indexers

Project Relocation

This was the original home of this project but the team has moved it to its official home at the following address https://github.com/azure-ai-foundry/mcp-foundry

Important Disclaimers

We are still implementing and testing out these capabilities.

Some of the tool groups described above are not yet available but would be coming soon.

We are continuously working on adding and upgrading the service capabilities. More tools are going to be added in the near future.

These tools have the ability to modify data in your AI Search Service.

Please note that all tools are currently marked experimental and may change behavior without advanced notice.

You should always review and verify all AI-generated content for accuracy and correctness.

Please proceed with caution and use at your own risk.

Available Tools

For the time being, the following tools are available from the service:

MCP Service Tool Groups

We do not want your MCP Host to be overwhelmed with the amount of tools coming from this service.

These are the available tool groups and their purposes:

• READ_OPERATIONS - tools used for read-only operations

• WRITE_OPERATIONS - tools that are used for creating, modifying or removing entries. Alias for ALL

• READ_INDEX - tools used to list and describe the indices in the service

• WRITE_INDEX - tools used to Create, delete, update, or configure indices

• READ_DOCUMENTS - tools used to query the indices to retrieve documents

• WRITE_DOCUMENTS - tools for Adding, Updating or Deleting documents from an index

• WRITE_INDEXERS - tools used to configure indexers, data sources & skill sets

• READ_INDEXERS - tools used to retrieve information about data sources, skill sets and indexers

Pre-Requisites

You will need to have configured a Service Principal that will be used to authentic against the AI Search service you are interacting with. You may also use the AI Search Key with read/write privilleges on the index dependening on what capabilities you are looking to leverage from the service.

Python 3.12 or later is needed, and you have to install uv as well to leverage the service.

The repository below contains a Terraform script that can accelerate the provisioning of an AI Search service and service principal necessary to help you get started.

Installing Dependencies

Follow the links below to install uv, python and the module containing the MCP service

• Installing uv

• Installing Python if Necessary with uv

You can install the mcp service as follows:

uv build

pip install dist/mcp_server_azure_ai_search_preview-0.3.1-py3-none-any.whl 

# If you are running in SSE mode, you can run it as:
uv run -m mcp_server_azure_ai_search_preview --transport sse --envFile .env --host 127.0.0.1 --port 8000

#f you are running it in STDIO mode in Cursor or Claude Desktop, we have an example config below for you to use

The environment file, host and ports are optional. The default values will be used if you do not specify them

You can also clone this git repo and install the service via the main.py file in this repo

Related MCP server: FreshMCP

Configuration of Environment Variables in MCP Host

If you are authenticating with a managed identity you will need to pass in the environment variables in your MCP host configuration. You can also use the AI Search API Key to Authenticate.

The authentication method and search endpoints needs to be specified. These are required environment variables

If you are authenticating with a service principal, then you should configure the following variable:

• AZURE_AUTHENTICATION_METHOD - default is "api-search-key"

• AZURE_AI_SEARCH_ENDPOINT

• AZURE_TENANT_ID

• AZURE_CLIENT_ID

• AZURE_CLIENT_SECRET

If you are authenticating with an API key, then you have to configure the following environment variables:

• AZURE_AUTHENTICATION_METHOD - default is "api-search-key"

• AZURE_AI_SEARCH_ENDPOINT

• AZURE_AUTHENTICATION_METHOD

• AZURE_AI_SEARCH_API_KEY

You can also filter the list of tools returned to your MCP host by specifying a comma-delimited list of tool groups in your configuration.

MCP Host Configuration in STDIO Mode

This is an example of the MCP configuration for VScode Agent Mode using the STDIO transport

{
    "inputs": [
        {
            "type": "promptString",
            "id": "AZURE_AI_SEARCH_API_KEY",
            "description": "AZURE_AI_SEARCH_API_KEY",
            "password": true
        },
        {
            "type": "promptString",
            "id": "LOCAL_WORKING_DIRECTORY",
            "description": "Directory where the MCP service Git repo was cloned to",
            "password": false
        }
    ],
    "servers": {

        "ai_search_mcp": {
            "type": "stdio",
            "command": "uv",

            "args": [
                "run",
                "--directory",
                "${input:LOCAL_WORKING_DIRECTORY}",
                "-m",
                "run mcp_server_azure_ai_search_preview"
                "--transport",
                "stdio"
            ],
            "env": {
                "AZURE_AI_SEARCH_MCP_TOOL_GROUPS": "ALL",
                "AZURE_AI_SEARCH_ENDPOINT": "https://{service_name}.search.windows.net",
                "AZURE_AI_SEARCH_API_VERSION": "2025-03-01-preview",
                "AZURE_AUTHENTICATION_METHOD": "api-search-key",
                "AZURE_AI_SEARCH_API_KEY":  "${input:AZURE_AI_SEARCH_API_KEY}"
            }
        }
    }
}

This example uses a custom environment variable file to specify the environment variables used by the MCP server

{
    "inputs": [
        {
            "type": "promptString",
            "id": "LOCAL_WORKING_DIRECTORY",
            "description": "Directory where the MCP service Git repo was cloned to",
            "password": false
        }
    ],
    "servers": {

        "ai_search_mcp": {
            "type": "stdio",
            "command": "uv",
            "args": [
                "run",
                "--directory",
                "${input:LOCAL_WORKING_DIRECTORY}",
                "-m",
                "run mcp_server_azure_ai_search_preview"
                "--transport",
                "stdio"
               "--envFile",
                ".env"
            ]
        }
    }
}

This example uses and MCP server running in SSE mode

# Start up the MCP server first and then specify this config in your `.vscode/mcp.json` file in your VSCODE workspace

uv run -m mcp_server_azure_ai_search_preview --transport sse --envFile .env

{
    "servers": {
        "ai_search_2": {
            "type": "sse",
            "url": "http://localhost:8000/sse"
        }
    }
}

Running from Agent Frameworks like Pydantic AI

You can follow the example in this repo for how to run the MCP client using a python Agent Framework like Pydantic AI

Ensure that all the SSE MCP servers that your client code depends on are up and running before you kick off the python MCP client