💻 AgentQL 允许用户通过定义所需信息的确切结构来抓取任何包含非结构化数据的网站。它提供一致的、结构化的结果，即使页面内容动态变化或布局频繁更改。

⚙️ 搭建AgentQL MCP服务器需要安装Node.js和AgentQL API，并配置Claude Desktop以连接到MCP服务器。配置过程包括创建AgentQL API密钥、安装必要的软件包以及修改claude_desktop_config.json文件。

📚 通过AgentQL，用户可以从Amazon搜索结果页面抓取AI书籍的详细信息。在Claude Desktop中，可以使用extract_web_data工具，输入URL和自然语言描述来提取数据，并在交互式终端中进行数据可视化，例如生成条形图和散点图。

💡 AgentQL可以与Notion或GitHub等其他服务器连接，自动发送结构化数据，用于文档、跟踪或进一步自动化。这使得AgentQL成为将非结构化网页内容转化为可操作见解的强大工具，这一切都通过简单的自然语言工作流程实现。

AgentQL allows you to scrape any website with unstructured data by defining the exact shape of the information you want. It gives you consistent, structured results—even from pages with dynamic content or frequently changing layouts.

In this tutorial, we’ll implement an AgentQL MCP server inside Claude Desktop, and use Claude’s built-in visualization capabilities to explore the data. Specifically, we’ll scrape an Amazon search results page for AI books, extracting details like price, rating, and number of reviews.

Step 1: Setting up dependencies

Node JS

We need npx to run the AgentQL server, which comes with Node.js.

Download the latest version of Node.js from nodejs.orgRun the installer.Leave all settings as default and complete the installation

Claude Desktop

Download Claude using https://claude.ai/download.

AgentQL API

Create your AgentQL API key at dev.agentql.com/api-keys and store it securely — you’ll need it later in this tutorial.

Step 2: Installing the packages

Once Node.js is installed, open your terminal and run the following command:

npm install -g agentql-mcp

Step 3: Configuring the MCP Server

Next, configure Claude to connect to your MCP server. Open the claude_desktop_config.json file located in the Claude installation directory using any text editor. If the file doesn’t exist, you can create it manually. Once opened, enter the following code:

{    "mcpServers": {      "agentql": {        "command": "npx",        "args": ["-y", "agentql-mcp"],        "env": {          "AGENTQL_API_KEY": "<YOUR_API_KEY>"        }      }    }  }

Replace <YOUR_API_KEY> with the key you generated.

Step 4: Running the server

Once the MCP configuration is complete, your server should appear in Claude. The AgentQL server includes a single powerful tool — extract_web_data — which takes a URL and a natural language description of the data structure you want to extract.

You can use any URL you want to scrape. For this tutorial, I used an Amazon search results page for AI books and asked Claude to visualize the extracted data. Claude provides an interactive terminal where it generates code to process and visualize the data — and you can edit that code as needed. Once the code was finalized, Claude presented a bar chart with interactive options to explore prices, ratings, review counts, and even a price vs. rating scatter plot, along with key summary statistics.

AgentQL can be used to scrape websites, and we can connect it with other servers like Notion or GitHub to automatically send structured data for documentation, tracking, or further automation.

This makes AgentQL a powerful tool for turning unstructured web content into actionable insights — all within a simple, natural language workflow.

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