Azure ADX

What is adx-mcp-server?

adx-mcp-server is a Model Context Protocol (MCP) server designed to enable AI assistants to interact with Azure Data Explorer (ADX) databases. It provides standardized interfaces for AI assistants to query, analyze, and explore data stored in ADX, including Eventhouse in Microsoft Fabric.

How to use adx-mcp-server?

To use adx-mcp-server, you need to:

1. Login to Azure: Authenticate your Azure account with permissions to the ADX cluster using Azure CLI.
2. Configure Environment Variables: Set ADX_CLUSTER_URL and ADX_DATABASE (and optionally AZURE_TENANT_ID, AZURE_CLIENT_ID, ADX_TOKEN_FILE_PATH for Workload Identity) either via a .env file or system environment variables.
3. Integrate with MCP Client: Add the server configuration to your MCP client (e.g., Claude Desktop) configuration file, specifying the command to run the server (e.g., uv or docker).

Docker Usage:

• Build Image: docker build -t adx-mcp-server .
• Run Directly: docker run -it --rm -e ADX_CLUSTER_URL=... -e ADX_DATABASE=... adx-mcp-server
• Docker Compose: Create a .env file and run docker-compose up.
• Claude Desktop with Docker: Configure Claude Desktop to use the Docker command with environment variables passed to the container.

Key Features of adx-mcp-server

• KQL Query Execution: Execute KQL queries against Azure Data Explorer.
• Database Resource Discovery:
  • List tables in the configured database.
  • View table schemas.
  • Sample data from tables.
  • Get table statistics/details.
• Authentication Support:
  • Token credential support (Azure CLI, MSI, etc.).
  • Workload Identity credential support for AKS.
• Docker Containerization: Easy deployment and isolation using Docker.
• Interactive Tools for AI Assistants: Provides configurable tools like execute_query, list_tables, get_table_schema, and sample_table_data.

Use Cases of adx-mcp-server

• AI-driven Data Analysis: Empower AI assistants to directly query and analyze large datasets in Azure Data Explorer.
• Automated Reporting: Generate reports and insights from ADX data programmatically through AI interactions.
• Data Exploration: Allow AI assistants to discover and understand the structure and content of ADX databases.
• Integration with MCP Clients: Seamlessly integrate ADX capabilities into applications that utilize the Model Context Protocol, such as Claude Desktop.

FAQ about adx-mcp-server

• What is MCP? MCP stands for Model Context Protocol, which defines standardized interfaces for AI assistants to interact with various data sources and tools.
• Does it support Azure Workload Identity? Yes, the server supports Workload Identity Credential by default when running in AKS environments with workload identity configured.
• How can I contribute? Contributions are welcome! You can open an issue or submit a pull request on the project's GitHub repository.
• What tools does it use for development? The project uses uv for dependency management and pytest for testing.
• How do I run tests? Install development dependencies with uv pip install -e ".[dev]" and then run pytest.

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

• Execute KQL queries against Azure Data Explorer

• Discover and explore database resources

  • List tables in the configured database
  • View table schemas
  • Sample data from tables
  • Get table statistics/details

• Authentication support

  • Token credential support (Azure CLI, MSI, etc.)
  • Workload Identity credential support for AKS

• Docker containerization support

• Provide interactive tools for AI assistants

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

1. Login to your Azure account which has the permission to the ADX cluster using Azure CLI.

2. 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

# Optional: Azure Workload Identity credentials 
# AZURE_TENANT_ID=your-tenant-id
# AZURE_CLIENT_ID=your-client-id 
# ADX_TOKEN_FILE_PATH=/var/run/secrets/azure/tokens/azure-identity-token

Azure Workload Identity Support

The server now uses WorkloadIdentityCredential by default when running in Azure Kubernetes Service (AKS) environments with workload identity configured. It prioritizes the use of WorkloadIdentityCredential whenever the necessary environment variables are present.

For AKS with Azure Workload Identity, you only need to:

1. Make sure the pod has AZURE_TENANT_ID and AZURE_CLIENT_ID environment variables set
2. Ensure the token file is mounted at the default path or specify a custom path with ADX_TOKEN_FILE_PATH

If these environment variables are not present, the server will automatically fall back to DefaultAzureCredential, which tries multiple authentication methods in sequence.

3. 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 \
  -e AZURE_TENANT_ID=your_tenant_id \
  -e AZURE_CLIENT_ID=your_client_id \
  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",
        "-e", "AZURE_TENANT_ID",
        "-e", "AZURE_CLIENT_ID",
        "-e", "ADX_TOKEN_FILE_PATH",
        "adx-mcp-server"
      ],
      "env": {
        "ADX_CLUSTER_URL": "https://yourcluster.region.kusto.windows.net",
        "ADX_DATABASE": "your_database",
        "AZURE_TENANT_ID": "your_tenant_id",
        "AZURE_CLIENT_ID": "your_client_id",
        "ADX_TOKEN_FILE_PATH": "/var/run/secrets/azure/tokens/azure-identity-token"
      }
    }
  }
}

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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