AgentQL MCP Server: Universal Data Extraction for AI Applications

Overview: What is AgentQL MCP Server?

AgentQL MCP Server is an essential component for integrating data extraction capabilities into various AI applications such as Claude Desktop, Continue, Cursor, and Windsurf via the Model Context Protocol (MCP). This server allows these applications to extract structured data from web pages using a standardized protocol. By leveraging this integration, developers can enhance the functionality of their AI applications, enabling them to perform tasks like extracting specific information from URLs, transforming unstructured data into structured formats, and integrating external services seamlessly.

🔧 Core Features & MCP Capabilities

AgentQL MCP Server supports core features such as web data extraction using a simple prompt description to define the fields to be extracted. The server can communicate with the AI application through standard protocol commands, ensuring seamless integration and optimal performance. This compatibility allows for flexible and powerful data handling, making it easier to develop robust AI workflows.

⚙️ MCP Architecture & Protocol Implementation

AgentQL MCP Server implements the MCP architecture by adhering to the Model Context Protocol standards. It uses a command-line interface (CLI) tool called extract-web-data, which interacts with the server through stdin and stdout channels. The protocol flow involves the AI application sending requests to the MCP server, which then processes these requests and returns structured data based on the provided URL and prompt.

🚀 Getting Started with Installation

To integrate AgentQL MCP Server into your AI applications, you need to install it globally using npm:

npm install -g agentql-mcp

After installation, configure the server and set up your API key as follows:

Configure Claude Desktop

1. Open Claude Desktop settings via ⌘+,.
2. Navigate to the Developer sidebar section.
3. Click Edit Config and open claude_desktop_config.json.
4. Add an entry for the AgentQL MCP server under mcpServers.

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

5. Save and restart the app.

Configure Cursor

1. Open Cursor Settings.
2. Go to the MCP > MCP Servers section.
3. Click + Add new MCP Server.
4. Enter the following:
   • Name: "agentql" or any preferred name
   • Type: "command"
   • Command: env AGENTQL_API_KEY=YOUR_API_KEY npx -y agentql-mcp

Configure Windsurf

1. Open Windsurf: MCP Configuration Panel.
2. Click Add custom server+.
3. Alternatively, open ~/.codeium/windsurf/mcp_config.json directly and add the following:

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

💡 Key Use Cases in AI Workflows

Use Case 1: Data Extraction for Marketing Analysis

Imagine you need to extract product information from a competitor's website. You can use AgentQL MCP Server to send a request with the appropriate prompt and URL. The server processes this request and returns structured data that includes the name, price, and availability of products, which can then be analyzed using marketing tools.

Use Case 2: Real Estate Data Collection

A real estate firm wishes to gather information about properties listed on a popular online platform. By integrating AgentQL MCP Server into their AI workflow, they can automatically extract details such as location, features, price, and images from the listings. This data can then be processed for further analysis or added to an internal database.

🔌 Integration with MCP Clients

AgentQL MCP Server is compatible with several MCP clients:

• Claude Desktop: Fully supported
• Continue: Fully supported
• Cursor: Fully supported
• Windsurf: Currently only supports tool integration

For optimal performance, ensure that your API key and environment variables are correctly configured across all relevant applications.

📊 Performance & Compatibility Matrix

The AgentQL MCP Server is designed to work seamlessly with various AI applications. The following compatibility matrix provides a quick reference:

MCP Client	Claude Desktop	Continue	Cursor	Windsurf
Resources	✅	✅	❌	✅
Tools	✅	✅	❌	✅
Prompts	✅	✅	❌	✅
Status	Full Support	Full Support	Tools Only	Partial Support

🛠️ Advanced Configuration & Security

For advanced users, the AgentQL MCP Server allows for custom configuration and debugging. Use the following commands to start the server with a custom setup:

{
  "mcpServers": {
    "agentql": {
      "command": "/path/to/agentql-mcp/dist/index.js",
      "env": {
        "AGENTQL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Ensure that you remove any default configurations to avoid potential conflicts.

❓ Frequently Asked Questions (FAQ)

Q1: Can Agents Use Tools?

Yes, agents can use tools for data extraction. However, some applications may need a hint like use tools or use agentql tool.

Q2: How Do I Validate MCP Integration?

Test the integration by giving your agent a task that involves web data extraction.

Q3: What is the MCP Inspector?

The MCP Inspector package script provides debugging tools to help troubleshoot issues.

Q4: Can I Use This Server Without An API Key?

No, an API key is required for full functionality and security reasons.

Q5: Are There Any Specific Requirements For Web Data Extraction Prompts?

Prompts should be clear and detailed. Include fields you want to extract in your prompt description.

👨‍💻 Development & Contribution Guidelines

To contribute to AgentQL MCP Server, ensure that you follow the existing coding standards and adhere to the project's best practices. Contributions can range from bug fixes to new features or improvements to documentation.

🌐 MCP Ecosystem & Resources

Explore more information about MCP and its ecosystem at:

• Model Context Protocol
• AgentQL Documentation

For developer resources, visit the official AgentQL and Model Context Protocol websites for tutorials, guides, and community support.

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By leveraging AgentQL MCP Server, developers can unlock powerful data extraction capabilities within their AI applications. This integration ensures that various AI tools are equipped to handle complex data tasks efficiently and accurately, enhancing overall application performance and functionality.