> ## 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.

# 如何使用 Microsoft Agent framework 和 ClickHouse MCP 服务器构建 AI Agent

> 了解如何使用 Microsoft Agent framework 和 ClickHouse MCP 服务器构建 AI Agent

在本指南中，你将学习如何构建一个 [Microsoft Agent framework](https://github.com/microsoft/agent-framework) AI agent，并通过 [ClickHouse MCP 服务器](https://github.com/ClickHouse/mcp-clickhouse) 与 [ClickHouse 的 SQL playground](https://sql.clickhouse.com/) 交互。

<Info>
  **示例笔记本**

  该示例的笔记本可在 [examples repository](https://github.com/ClickHouse/examples/blob/main/ai/mcp/microsoft-agent-framework/microsoft-agent-framework.ipynb) 中找到。
</Info>

<div id="prerequisites">
  ## 前置条件
</div>

* 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 OpenAI API 密钥

你可以在 Python REPL 中或通过脚本执行以下步骤。

<Steps>
  <Step title="安装相关库" id="install-libraries">
    运行以下命令安装 Microsoft Agent 框架库：

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

  <Step title="配置凭据" id="setup-credentials">
    接下来，您需要提供 OpenAI API 密钥：

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

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

    接下来，配置连接到 ClickHouse SQL playground 所需的凭据：

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

  <Step title="初始化 MCP 服务器和 Microsoft Agent Framework 智能体" id="initialize-mcp-and-agent">
    现在，将 ClickHouse MCP 服务器配置为指向 ClickHouse SQL playground，
    并初始化我们的智能体，然后向它提一个问题：

    ```python theme={null}
    from agent_framework import ChatAgent, MCPStdioTool
    from agent_framework.openai import OpenAIResponsesClient
    ```

    ```python theme={null}
    clickhouse_mcp_server = MCPStdioTool(
        name="clickhouse",
        command="uv",
        args=[
            "run",
            "--with",
            "mcp-clickhouse",
            "--python",
            "3.10",
            "mcp-clickhouse"
        ],
        env=env
    )

    async with ChatAgent(
        chat_client=OpenAIResponsesClient(model_id="gpt-5-mini-2025-08-07"),
        name="HousePricesAgent",
        instructions="You are a helpful assistant that can help query a ClickHouse database",
        tools=clickhouse_mcp_server,
    ) as agent:
        query = "Tell me about UK property prices over the last five years"
        print(f"User: {query}")
        async for chunk in agent.run_stream(query):
            print(chunk.text, end="", flush=True)
        print("\n\n")
    ```

    运行此脚本后的输出如下：

    ```response title="Response" theme={null}
    User: Tell me about UK property prices over the last five years
    I looked at monthly UK sold-price records in the uk.uk_price_paid_simple_partitioned table for the last five years (toStartOfMonth(date), from Oct 2020 → Aug 2025). Summary and key points:

    What I measured
    - Metrics: monthly median price, mean price, and transaction count (price paid records).
    - Period covered: months starting 2020-10-01 through 2025-08-01 (last five years from today).

    High-level findings
    - Median price rose from £255,000 (2020-10) to £294,500 (2025-08) — an increase of about +15.4% over five years.
      - Equivalent compound annual growth rate (CAGR) for the median ≈ +2.9% per year.
    - Mean price fell slightly from about £376,538 (2020-10) to £364,653 (2025-08) — a decline of ≈ −3.2% over five years.
      - Mean-price CAGR ≈ −0.6% per year.
    - The divergence (median up, mean slightly down) suggests changes in the mix of transactions (fewer very-high-value sales or other compositional effects), since the mean is sensitive to outliers while the median is not.

    Notable patterns and events in the data
    - Strong rises in 2020–2021 (visible in both median and mean), consistent with the post‑pandemic / stamp‑duty / demand-driven market surge seen in that period.
    - Peaks in mean prices around mid‑2022 (mean values ~£440k), then a general softening through 2022–2023 and stabilisation around 2023–2024.
    - Some months show large volatility or unusual counts (e.g., June 2021 and June 2021 had very high transaction counts; March 2025 shows a high median but April–May 2025 show lower counts). Recent months (mid‑2025) have much lower transaction counts in the table — this often indicates incomplete reporting for the most recent months and means recent monthly figures should be treated cautiously.

    Example datapoints (from the query)
    - 2020-10: median £255,000, mean £376,538, transactions 89,125
    - 2022-08: mean peak ~£441,209 (median ~£295,000)
    - 2025-03: median ~£314,750 (one of the highest medians)
    - 2025-08: median £294,500, mean £364,653, transactions 18,815 (low count — likely incomplete)

    Caveats
    - These are transaction prices (Price Paid dataset) — actual house “values” may differ.
    - Mean is sensitive to composition and outliers. Changes in the types of properties sold (e.g., mix of flats vs detached houses, regional mix) will affect mean and median differently.
    - Recent months can be incomplete; months with unusually low transaction counts should be treated with caution.
    - This is a national aggregate — regional differences can be substantial.

    If you want I can:
    - Produce a chart of median and mean over time.
    - Compare year-on-year or compute CAGR for a different start/end month.
    - Break the analysis down by region/county/town, property type (flat, terraced, semi, detached), or by price bands.
    - Show a table of top/bottom regions for price growth over the last 5 years.

    Which follow-up would you like?

    ```
  </Step>
</Steps>