Azure ADX

community

Query and analyze Azure Data Explorer databases.

Azure Data Explorer MCP Server

A Model Context Protocol (MCP) server for Azure Data Explorer/Eventhouse in Microsoft Fabric.

This provides access to your Azure Data Explorer/Eventhouse clusters and databases through standardized MCP interfaces, allowing AI assistants to execute KQL queries and explore your data.

Features

The list of tools is configurable, so you can choose which tools you want to make available to the MCP client. This is useful if you don't use certain functionality or if you don't want to take up too much of the context window.

Usage

Login to your Azure account which has the permission to the ADX cluster using Azure CLI.
Configure the environment variables for your ADX cluster, either through a
```
.env
```
file or system environment variables:

# Required: Azure Data Explorer configuration
ADX_CLUSTER_URL=https://yourcluster.region.kusto.windows.net
ADX_DATABASE=your_database

Add the server configuration to your client configuration file. For example, for Claude Desktop:

{
  "mcpServers": {
    "adx": {
      "command": "uv",
      "args": [
        "--directory",
        "<full path to adx-mcp-server directory>",
        "run",
        "src/adx_mcp_server/main.py"
      ],
      "env": {
        "ADX_CLUSTER_URL": "https://yourcluster.region.kusto.windows.net",
        "ADX_DATABASE": "your_database"
      }
    }
  }
}

Note: if you see
Error: spawn uv ENOENT
in Claude Desktop, you may need to specify the full path to
uv
or set the environment variable
NO_UV=1
in the configuration.

Docker Usage

This project includes Docker support for easy deployment and isolation.

Building the Docker Image

Build the Docker image using:

docker build -t adx-mcp-server .

Running with Docker

You can run the server using Docker in several ways:

Using docker run directly:

docker run -it --rm \
  -e ADX_CLUSTER_URL=https://yourcluster.region.kusto.windows.net \
  -e ADX_DATABASE=your_database \
  adx-mcp-server

Using docker-compose:

Create a

.env

file with your Azure Data Explorer credentials and then run:

docker-compose up

Running with Docker in Claude Desktop

To use the containerized server with Claude Desktop, update the configuration to use Docker with the environment variables:

{
  "mcpServers": {
    "adx": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "ADX_CLUSTER_URL",
        "-e", "ADX_DATABASE",
        "adx-mcp-server"
      ],
      "env": {
        "ADX_CLUSTER_URL": "https://yourcluster.region.kusto.windows.net",
        "ADX_DATABASE": "your_database"
      }
    }
  }
}

This configuration passes the environment variables from Claude Desktop to the Docker container by using the

-e

flag with just the variable name, and providing the actual values in the

env

object.

Using as a Dev Container / GitHub Codespace

This repository can also be used as a development container for a seamless development experience. The dev container setup is located in the

devcontainer-feature/adx-mcp-server

folder.

For more details, check the devcontainer README.

Development

Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.

This project uses

uv

to manage dependencies. Install

uv

following the instructions for your platform:

curl -LsSf https://astral.sh/uv/install.sh | sh

You can then create a virtual environment and install the dependencies with:

uv venv
source .venv/bin/activate  # On Unix/macOS
.venv\Scripts\activate     # On Windows
uv pip install -e .

Project Structure

The project has been organized with a

src

directory structure:

adx-mcp-server/
├── src/
│   └── adx_mcp_server/
│       ├── __init__.py      # Package initialization
│       ├── server.py        # MCP server implementation
│       ├── main.py          # Main application logic
├── Dockerfile               # Docker configuration
├── docker-compose.yml       # Docker Compose configuration
├── .dockerignore            # Docker ignore file
├── pyproject.toml           # Project configuration
└── README.md                # This file

Testing

The project includes a comprehensive test suite that ensures functionality and helps prevent regressions.

Run the tests with pytest:

# Install development dependencies
uv pip install -e ".[dev]"

# Run the tests
pytest

# Run with coverage report
pytest --cov=src --cov-report=term-missing

Tests are organized into:

Configuration validation tests
Server functionality tests
Error handling tests
Main application tests

When adding new features, please also add corresponding tests.

Tools

Tool	Category	Description
`execute_query`	Query	Execute a KQL query against Azure Data Explorer
`list_tables`	Discovery	List all tables in the configured database
`get_table_schema`	Discovery	Get the schema for a specific table
`sample_table_data`	Discovery	Get sample data from a table with optional sample size

License

MIT

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