AgentQL MCP Server

What does “AgentQL” MCP Server do?

The AgentQL MCP Server is a Model Context Protocol (MCP) server designed to integrate AgentQL’s advanced data extraction capabilities into AI-powered development workflows. By acting as a bridge between AI assistants and web data, it enables seamless extraction of structured information from web pages using customizable prompts. This empowers developers and AI clients to automate tasks such as web data extraction, context gathering, and structured information retrieval for use in downstream applications or workflows. The AgentQL MCP Server is particularly useful for scenarios where real-time or on-demand access to external, web-based datasets is required, enhancing the power and flexibility of AI assistants in coding, research, and automation environments.

List of Prompts

No explicit prompt templates are mentioned in the repository.

List of Resources

No explicit resources are mentioned in the repository.

List of Tools

• extract-web-data
  Extracts structured data from a specified URL. The tool uses a ‘prompt’ as a description of the data and the fields to extract, enabling targeted and flexible web data extraction.

Use Cases of this MCP Server

• Web Data Extraction for Research
  Quickly extract tables, lists, or structured information from web pages to accelerate research, reporting, or data aggregation tasks.

• Automated Content Gathering
  Integrate into workflows to automatically retrieve and structure content from specific URLs as part of a content pipeline or knowledge management system.

• AI-Powered Workflow Automation
  Enable AI assistants (in tools like Claude or VS Code) to fetch real-time data from the web and use it as context for coding, analysis, or decision-making.

• Form and Field Extraction
  Automate the extraction of key fields or form data from web-based sources for further processing or integration into databases.

How to set it up

Windsurf

No setup instructions provided for Windsurf in the repository.

Claude

1. Open Claude Desktop Settings via ⌘ + , (not Account Settings).
2. Go to the Developer sidebar section.
3. Click Edit Config to open the claude_desktop_config.json file.
4. Add the AgentQL MCP Server inside the mcpServers dictionary in the config file:

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

5. Restart the app.

Note: Secure your API key using environment variables as shown above.

Cursor

No setup instructions provided for Cursor in the repository.

Cline

No setup instructions provided for Cline in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "agentql": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “agentql” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

• Roots support: Not mentioned
• Sampling support: Not mentioned

Our opinion

AgentQL MCP Server is a focused tool for web data extraction via MCP, with simple setup for Claude and VS Code. Documentation is concise but lacks details on prompts, resources, or advanced MCP features such as roots and sampling. Still, the presence of a working tool and clear API key handling are strengths. It scores well for basic utility but could be improved with more comprehensive MCP integration and documentation.

MCP Score