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AgentQL MCP Server: Smart Workflows & Precision Extraction - MCP Implementation

AgentQL MCP Server: Smart Workflows & Precision Extraction

Unleash data potential with AgentQL MCP Server! Merging Model Context Protocol and AgentQL's extraction magic for seamless smart workflows. Precision meets power—your AI’s new go-to." )

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About AgentQL MCP Server

What is AgentQL MCP Server: Smart Workflows & Precision Extraction?

AgentQL MCP Server is an advanced middleware solution engineered to seamlessly integrate AI-driven data extraction capabilities into Model Context Protocol (MCP)-compliant platforms. By leveraging [AgentQL](https://agentql.com)'s core strengths, it enables developers to automate the retrieval and structuring of web-based data with surgical precision, eliminating manual intervention while ensuring contextual accuracy.

How to Use AgentQL MCP Server: Smart Workflows & Precision Extraction?

Adoption follows a three-stage process: global installation via npm, API key configuration tailored to your workspace, and platform-specific setup adjustments. Begin by installing the server globally, then inject your credentials into the environment variables of supported tools like Claude Desktop, Cursor, or Windsurf. Post-configuration, trigger extractions by embedding structured data requests within your agent's tasks using natural language prompts.

AgentQL MCP Server Features

Key Features of AgentQL MCP Server: Smart Workflows & Precision Extraction?

  • Context-Aware Extraction: Dynamically maps user intents to precise data fields through declarative prompts
  • Adaptive Integration: Supports major MCP clients with platform-optimized configuration profiles
  • Debugging Framework: Built-in traceability for failed extractions with granular error reporting
  • Enterprise-Ready: Scalable architecture with role-based access controls for API endpoints

Use Cases of AgentQL MCP Server: Smart Workflows & Precision Extraction?

Empowers scenarios ranging from competitive intelligence aggregation (e.g., real-time price tracking) to automated content curation systems. Particularly valuable for:

  • Market research platforms needing structured data from heterogeneous sources
  • Customer support systems requiring ticket metadata parsing
  • Content management workflows demanding semantic data enrichment

AgentQL MCP Server FAQ

FAQ from AgentQL MCP Server: Smart Workflows & Precision Extraction?

Q: How does it handle dynamically changing web page structures?
A: Built-in DOM monitoring triggers adaptive parsing strategies when layouts shift, maintaining extraction accuracy without manual recoding.

Q: Can it extract data from JavaScript-heavy SPAs?
A: Yes, via headless browser integration that renders interactive content prior to extraction.

Q: What guarantees data consistency across platforms?
A: The MCP-compliant abstraction layer ensures uniform output formatting regardless of client implementation differences.

Content

AgentQL MCP Server

This is a Model Context Protocol (MCP) server that integrates AgentQL's data extraction capabilities.

Features

Tools

  • extract-web-data - extract structured data from a given 'url', using 'prompt' as a description of actual data and its fields to extract.

Installation

To use AgentQL MCP Server to extract data from web pages, you need to install it via npm, get an API key from our Dev Portal, and configure it in your favorite app that supports MCP.

Install the package

npm install -g agentql-mcp

Configure Claude

  • Open Claude Desktop Settings via +, (don't confuse with Claude Account Settings)

  • Go to Developer sidebar section

  • Click Edit Config and open claude_desktop_config.json file

  • Add agentql server inside mcpServers dictionary in the config file

  • Restart the app

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

Read more about MCP configuration in Claude here.

Configure Cursor

  • Open Cursor Settings
  • Go to MCP > MCP Servers
  • Click + Add new MCP Server
  • Enter the following:
    • Name: "agentql" (or your preferred name)
    • Type: "command"
    • Command: env AGENTQL_API_KEY=YOUR_API_KEY npx -y agentql-mcp

Read more about MCP configuration in Cursor here.

Configure Windsurf

  • Open Windsurf: MCP Configuration Panel

  • Click Add custom server+

  • Alternatively you can open ~/.codeium/windsurf/mcp_config.json directly

  • Add agentql server inside mcpServers dictionary in the config file

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

Read more about MCP configuration in Windsurf here.

Validate MCP integration

Give your agent a task that will require extracting data from the web. For example:

Extract the list of videos from the page https://www.youtube.com/results?search_query=agentql, every video should have a title, an author name, a number of views and a url to the video. Make sure to exclude ads items. Format this as a markdown table.

[!TIP] In case your agent complains that it can't open urls or load content from the web instead of using AgentQL, try adding "use tools" or "use agentql tool" hint.

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

If you want to try out development version, you can use the following config instead of the default one:

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

[!NOTE] Don't forget to remove the default AgentQL MCP server config to not confuse Claude with two similar servers.

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

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