Azure 数据探测器 Mcp 服务器

什么是 ADX-MCP-Server？

ADX-MCP-Server 是一个模型上下文协议（MCP）服务器，旨在帮助 AI 助手查询和分析 Azure 数据探索器（ADX）数据库。它提供标准化接口，简化 AI 系统与数据存储库之间的交互，使有效检索和操作数据变得更加容易。

ADX-MCP-Server 的特点

• 标准化接口：该服务器提供一组标准化的 API，允许 AI 助手与 Azure 数据探索器数据库之间无缝通信。
• 数据查询：用户可以在 ADX 数据库上执行复杂查询，从而实现高效的数据检索和分析。
• AI 集成：该服务器旨在支持 AI 应用程序，增强其处理和分析大数据集的能力。
• 公共存储库：ADX-MCP-Server 作为公共存储库提供，允许开发者贡献并增强其功能。
• MIT 许可证：该项目是开源的，并在 MIT 许可证下授权，促进开发者社区内的合作与共享。

如何使用 ADX-MCP-Server

1. 安装：从 GitHub 克隆存储库，并按照文档中提供的安装说明进行操作。
2. 配置：通过配置必要的参数来设置服务器，以连接到您的 Azure 数据探索器实例。
3. API 访问：利用标准化的 API 发送查询并接收来自 ADX 数据库的响应。
4. 集成：将服务器与您的 AI 应用程序集成，以利用其查询能力进行数据分析。

常见问题解答

Q1: ADX-MCP-Server 的目的是什么？

A1: ADX-MCP-Server 作为 AI 助手与 Azure 数据探索器数据库之间的桥梁，通过标准化接口实现高效的数据查询和分析。

Q2: ADX-MCP-Server 是开源的吗？

A2: 是的，ADX-MCP-Server 是一个开源项目，托管在 GitHub 上，采用 MIT 许可证。

Q3: 我可以为 ADX-MCP-Server 贡献吗？

A3: 当然可以！欢迎贡献。您可以分叉存储库，进行更改，并提交拉取请求。

Q4: 我该如何安装 ADX-MCP-Server？

A4: 您可以通过从 GitHub 克隆存储库并按照文档中的安装说明进行操作来安装服务器。

Q5: 我可以使用 ADX-MCP-Server 运行什么类型的查询？

A5: 您可以运行各种查询，包括复杂的分析查询，以检索和操作存储在 Azure 数据探索器数据库中的数据。

Azure Data Explorer MCP Server

<a href="https://glama.ai/mcp/servers/1yysyd147h"> <img width="380" height="200" src="https://glama.ai/mcp/servers/1yysyd147h/badge" /> </a>

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