ClickHouse MCP Server を使用して LlamaIndex AI エージェントを構築する方法
このガイドでは、ClickHouse の SQL Playground と対話できるようにするために、ClickHouse MCP Server を利用した LlamaIndex AI エージェントの構築方法を説明します。
サンプルノートブック
このサンプルは、examples リポジトリ 内のノートブックとして利用できます。
前提条件
- システムにPythonがインストールされていること
- システムに
pipがインストールされていること - AnthropicのAPIキー、または他のLLMプロバイダーのAPIキー
以下の手順は、Python REPLまたはスクリプトから実行できます。
ライブラリのインストール
次のコマンドを実行して、必要なライブラリをインストールします。
pip install -q --upgrade pip
pip install -q llama-index clickhouse-connect llama-index-llms-anthropic llama-index-tools-mcp
資格情報の設定
次に、Anthropic の API キーを設定する必要があります。
import os, getpass
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")
Enter Anthropic API Key: ········
別の LLM プロバイダーを使用する場合
Anthropic の API キーを持っておらず、別の LLM プロバイダーを使用したい場合は、 認証情報の設定方法については LlamaIndex「LLMs」ドキュメント を参照してください。
MCP Server を初期化する
次に、ClickHouse MCP Server を構成し、ClickHouse SQL playground を接続先として指定します。 これらを Python の関数から LlamaIndex のツールへ変換する必要があります。
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()
tools = await mcp_tool_spec.to_tool_list_async()
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)
```python
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)
```python
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-sonnet-4-0")
```python
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-sonnet-4-0")
```python
response = agent.query("What's the most popular repository?")
```python
response = agent.query("What's the most popular repository?")
```response title="Response"
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.
```response title="Response"
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.
