> ## Documentation Index
> Fetch the complete documentation index at: https://clickhouse.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How to build an AI Agent with Claude Agent SDK and the ClickHouse MCP server

> Learn how build an AI Agent with Claude Agent SDK and the ClickHouse MCP server

In this guide you'll learn how to build a [Claude Agent SDK](https://docs.claude.com/en/api/agent-sdk/overview) AI agent that can interact with
[ClickHouse's SQL playground](https://sql.clickhouse.com/) using [ClickHouse's MCP server](https://github.com/ClickHouse/mcp-clickhouse).

<Info>
  **Example notebook**

  This example can be found as a notebook in the [examples repository](https://github.com/ClickHouse/examples/blob/main/ai/mcp/claude-agent/claude-agent.ipynb).
</Info>

<h2 id="prerequisites">
  Prerequisites
</h2>

* You'll need to have Python installed on your system.
* You'll need to have `pip` installed on your system.
* You'll need an Anthropic API key

You can run the following steps either from your Python REPL or via script.

<Steps>
  <Step title="Install libraries" id="install-libraries">
    Install the Claude Agent SDK library by running the following commands:

    ```python theme={null}
    pip install -q --upgrade pip
    pip install -q claude-agent-sdk
    pip install -q ipywidgets
    ```
  </Step>

  <Step title="Setup credentials" id="setup-credentials">
    Next, you'll need to provide your Anthropic API key:

    ```python theme={null}
    import os, getpass
    os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")
    ```

    ```response title="Response" theme={null}
    Enter Anthropic API Key: ········
    ```

    Next, define the credentials needed to connect to the ClickHouse SQL playground:

    ```python theme={null}
    env = {
        "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
        "CLICKHOUSE_PORT": "8443",
        "CLICKHOUSE_USER": "demo",
        "CLICKHOUSE_PASSWORD": "",
        "CLICKHOUSE_SECURE": "true"
    }
    ```
  </Step>

  <Step title="Initialize MCP server and Claude Agent SDK agent" id="initialize-mcp-and-agent">
    Now configure the ClickHouse MCP server to point at the ClickHouse SQL playground
    and also initialize our agent and ask it a question:

    ```python theme={null}
    from claude_agent_sdk import query, ClaudeAgentOptions, AssistantMessage, UserMessage, TextBlock, ToolUseBlock
    ```

    ```python theme={null}
    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)
    ```

    Note the code inside the `for` block is filtering the output for brevity.

    ```response title="Response" theme={null}
    🤖 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.
    ```
  </Step>
</Steps>