Azure Data Explorer MCP Server

A Model Context Protocol (MCP) server for Azure Data Explorer.

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

Features

[x] Execute KQL queries against Azure Data Explorer
[x] Discover and explore database resources
- [x] List tables in the configured database
- [x] View table schemas
- [x] Sample data from tables
[x] Authentication support
- [x] Client credentials from environment variables
[x] 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

Create a service account in Azure Data Explorer with appropriate permissions, or ensure you have access through your Azure account.
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 authentication (if not using default credentials)
AZURE_TENANT_ID=your_tenant_id
AZURE_CLIENT_ID=your_client_id
AZURE_CLIENT_SECRET=your_client_secret

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.

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
├── 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

# Or use the convenience script
./run_tests.sh
./run_tests.sh --coverage  # With coverage

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 |

Example Usage

Here's how an AI assistant might use this MCP server to interact with your Azure Data Explorer instance:

User: I need to analyze some data in Azure Data Explorer. Can you help me?

AI: I can help you analyze data in Azure Data Explorer. Let me first check what tables are available in your database.
[AI uses the list_tables tool]

AI: I found several tables in your database. Here are the available tables:
- logs
- metrics
- users
- events

What kind of analysis would you like to perform?

User: I want to see if there were any errors in the logs in the last 24 hours.

AI: I'll query the logs table for errors in the last 24 hours.
[AI uses the execute_query tool with:
  "logs | where Timestamp > ago(24h) and Level == 'Error' | summarize count() by Source"]

AI: Here are the error counts by source from the last 24 hours:
| Source      | count |
|-------------|-------|
| WebService  | 42    |
| DataProcessor| 17   |
| API         | 5     |

Would you like me to investigate any of these error sources in more detail?

License

MIT