如何使用 Claude Agent SDK 和 ClickHouse MCP Server 构建 AI Agent

在本指南中，您将学习如何使用 ClickHouse 的 MCP Server，构建一个可以与 ClickHouse 的 SQL Playground 交互的 Claude Agent SDK AI Agent。

示例笔记本

该示例可以在 examples 仓库 中找到对应的笔记本。

前提条件

• 您需要在系统上安装 Python。
• 您需要在系统上安装 pip。
• 您需要一个 Anthropic API 密钥。

您可以在 Python REPL 中或通过脚本来执行以下步骤。

安装库

运行以下命令安装 Claude Agent SDK 库：

pip install -q --upgrade pip
pip install -q claude-agent-sdk
pip install -q ipywidgets

设置凭据

接下来，您需要提供 Anthropic API 密钥：

import os, getpass
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")

Enter Anthropic API Key: ········

接下来，定义用于连接 ClickHouse SQL Playground 所需的凭据：

env = {
    "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
    "CLICKHOUSE_PORT": "8443",
    "CLICKHOUSE_USER": "demo",
    "CLICKHOUSE_PASSWORD": "",
    "CLICKHOUSE_SECURE": "true"
}

初始化 MCP 服务器和 Claude Agent SDK 代理

现在将 ClickHouse MCP 服务器配置为指向 ClickHouse SQL playground，然后初始化我们的代理并向它提出一个问题：

from claude_agent_sdk import query, ClaudeAgentOptions, AssistantMessage, UserMessage, TextBlock, ToolUseBlock

options = ClaudeAgentOptions(
    allowed_tools=[
        "mcp__mcp-clickhouse__list_databases",
        "mcp__mcp-clickhouse__list_tables", 
        "mcp__mcp-clickhouse__run_select_query",
        "mcp__mcp-clickhouse__run_chdb_select_query"
    ],
    mcp_servers={
        "mcp-clickhouse": {
            "command": "uv",
            "args": [
                "run",
                "--with", "mcp-clickhouse",
                "--python", "3.10",
                "mcp-clickhouse"
            ],
            "env": env
        }
    }
)


async for message in query(prompt="Tell me something interesting about UK property sales", options=options):
    if isinstance(message, AssistantMessage):
        for block in message.content:
            if isinstance(block, TextBlock):
                print(f"🤖 {block.text}")
            if isinstance(block, ToolUseBlock):
                print(f"🛠️ {block.name} {block.input}")
    elif isinstance(message, UserMessage):
        for block in message.content:
            if isinstance(block, TextBlock):
                print(block.text)

请注意，for 块中的代码会对输出进行过滤，以保持简洁。

🤖 I'll query the ClickHouse database to find something interesting about UK property sales.

Let me first see what databases are available:
🛠️ mcp__mcp-clickhouse__list_databases {}
🤖 Great! There's a "uk" database. Let me see what tables are available:
🛠️ mcp__mcp-clickhouse__list_tables {'database': 'uk'}
🤖 Perfect! The `uk_price_paid` table has over 30 million property sales records. Let me find something interesting:
🛠️ mcp__mcp-clickhouse__run_select_query {'query': "\nSELECT \n    street,\n    town,\n    max(price) as max_price,\n    min(price) as min_price,\n    max(price) - min(price) as price_difference,\n    count() as sales_count\nFROM uk.uk_price_paid\nWHERE street != ''\nGROUP BY street, town\nHAVING sales_count > 100\nORDER BY price_difference DESC\nLIMIT 1\n"}
🤖 Here's something fascinating: **Baker Street in London** (yes, the famous Sherlock Holmes street!) has the largest price range of any street with over 100 sales - properties sold for as low as **£2,500** and as high as **£594.3 million**, a staggering difference of over £594 million!

This makes sense given Baker Street is one of London's most prestigious addresses, running through wealthy areas like Marylebone, and has had 541 recorded sales in this dataset.