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如何使用 ClickHouse MCP 服务器构建 LlamaIndex AI 智能体

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在本指南中，你将学习如何构建一个 LlamaIndex AI 智能体，使其能够使用 ClickHouse 的 MCP 服务器 与 ClickHouse 的 SQL playground 交互。

前置条件

  • 你的系统中需要已安装 Python。
  • 你的系统中需要已安装 pip。
  • 你需要有一个 Anthropic API key，或其他 LLM 提供商的 API key

你可以在 Python REPL 中或通过脚本执行以下步骤。

安装库

运行以下命令，安装所需的库：

pip install -q --upgrade pip
pip install -q llama-index clickhouse-connect llama-index-llms-anthropic llama-index-tools-mcp

设置凭证

接下来，你需要提供 Anthropic 的 API 密钥：

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

初始化 MCP 服务器

现在将 ClickHouse MCP 服务器配置为指向 ClickHouse SQL playground。 你需要将这些 Python 函数转换为 Llama Index 工具：

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

mcp_client = BasicMCPClient(
    "uv",
    args=[
        "run",
        "--with", "mcp-clickhouse",
        "--python", "3.13",
        "mcp-clickhouse"
    ],
    env={
        "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
        "CLICKHOUSE_PORT": "8443",
        "CLICKHOUSE_USER": "demo",
        "CLICKHOUSE_PASSWORD": "",
        "CLICKHOUSE_SECURE": "true"
    }
)

mcp_tool_spec = McpToolSpec(
    client=mcp_client,
)

tools = await mcp_tool_spec.to_tool_list_async()

创建一个 agent

现在，你已经可以创建一个能够访问这些工具的 agent。将单次运行中的工具调用次数上限设为 10。如有需要，你可以修改这个参数：

from llama_index.core.agent import AgentRunner, FunctionCallingAgentWorker

agent_worker = FunctionCallingAgentWorker.from_tools(
    tools=tools,
    llm=llm, verbose=True, max_function_calls=10
)
agent = AgentRunner(agent_worker)

初始化 LLM

使用以下代码初始化 Claude Sonnet 4.0：

from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-sonnet-4-0")

运行 agent

最后，你可以向 agent 提出一个问题：

response = agent.query("What's the most popular repository?")

返回的响应较长，因此在下面的示例中 进行了截断：

Responseresponse
Added user message to memory: What's the most popular repository?
=== LLM Response ===
I'll help you find the most popular repository. Let me first explore the available databases and tables to understand the data structure.
=== Calling Function ===
Calling function: list_databases with args: {}
=== Function Output ===
meta=None content=[TextContent(type='text', text='amazon\nbluesky\ncountry\ncovid\ndefault\ndns\nenvironmental\nfood\nforex\ngeo\ngit\ngithub\nhackernews\nimdb\nlogs\nmetrica\nmgbench\nmta\nnoaa\nnyc_taxi\nnypd\nontime\nopensky\notel\notel_v2\npypi\nrandom\nreddit\nrubygems\nstackoverflow\nstar_schema\nstock\nsystem\ntw_weather\ntwitter\nuk\nwiki\nwords\nyoutube', annotations=None)] isError=False
=== LLM Response ===
I can see there's a `github` database which likely contains repository data. Let me explore the tables in that database.
=== Calling Function ===
Calling function: list_tables with args: {"database": "github"}
=== Function Output ===
...
...
...
=== LLM Response ===
Based on the GitHub data, **the most popular repository is `sindresorhus/awesome`** with **402,292 stars**.

Here are the top 10 most popular repositories by star count:

1. **sindresorhus/awesome** - 402,292 stars
2. **996icu/996.ICU** - 388,413 stars  
3. **kamranahmedse/developer-roadmap** - 349,097 stars
4. **donnemartin/system-design-primer** - 316,524 stars
5. **jwasham/coding-interview-university** - 313,767 stars
6. **public-apis/public-apis** - 307,227 stars
7. **EbookFoundation/free-programming-books** - 298,890 stars
8. **facebook/react** - 286,034 stars
9. **vinta/awesome-python** - 269,320 stars
10. **freeCodeCamp/freeCodeCamp** - 261,824 stars

The `sindresorhus/awesome` repository is a curated list of awesome lists, which explains its popularity as it serves as a comprehensive directory of resources across many different topics in software development.
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