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AgentQL MCP Server
What is AgentQL MCP Server
AgentQL MCP Server is a Model Context Protocol server designed to integrate the data extraction capabilities of AgentQL. It allows users to extract structured data from web pages based on specified prompts, making it a useful tool for gathering online information efficiently.
Use cases
The primary use cases for AgentQL MCP Server include web data extraction for research, business intelligence, content aggregation, and other scenarios where structured data needs to be collected from websites. It can be utilized in various applications such as chatbots, data analysis tools, and automated reporting systems.
How to use
To use the AgentQL MCP Server, you first need to install the package via npm and obtain an API key from the AgentQL Dev Portal. After configuring the server in your development environment (like Claude, VS Code, Cursor, or Windsurf), you can issue commands that describe the data you want to extract from web pages. Prompts should specify the required data fields clearly.
Key features
Key features of AgentQL MCP Server include the ability to extract data using custom prompts, support for various configurations across different development environments, and the potential to format data output neatly (e.g., as markdown tables). It facilitates efficient data gathering by automating web scraping tasks.
Where to use
AgentQL MCP Server can be used in any application that supports Model Context Protocol (MCP). It is compatible with development environments such as Claude, VS Code, Cursor, and Windsurf, making it a versatile tool for developers seeking to implement web data extraction functionality in their projects.
Overview
What is AgentQL MCP Server
AgentQL MCP Server is a Model Context Protocol server designed to integrate the data extraction capabilities of AgentQL. It allows users to extract structured data from web pages based on specified prompts, making it a useful tool for gathering online information efficiently.
Use cases
The primary use cases for AgentQL MCP Server include web data extraction for research, business intelligence, content aggregation, and other scenarios where structured data needs to be collected from websites. It can be utilized in various applications such as chatbots, data analysis tools, and automated reporting systems.
How to use
To use the AgentQL MCP Server, you first need to install the package via npm and obtain an API key from the AgentQL Dev Portal. After configuring the server in your development environment (like Claude, VS Code, Cursor, or Windsurf), you can issue commands that describe the data you want to extract from web pages. Prompts should specify the required data fields clearly.
Key features
Key features of AgentQL MCP Server include the ability to extract data using custom prompts, support for various configurations across different development environments, and the potential to format data output neatly (e.g., as markdown tables). It facilitates efficient data gathering by automating web scraping tasks.
Where to use
AgentQL MCP Server can be used in any application that supports Model Context Protocol (MCP). It is compatible with development environments such as Claude, VS Code, Cursor, and Windsurf, making it a versatile tool for developers seeking to implement web data extraction functionality in their projects.
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 insidemcpServers
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 VS Code
For one-click installation, click one of the install buttons below:
Manual Installation
Click the install buttons at the top of this section for the quickest installation method. For manual installation, follow these steps:
Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P
and typing Preferences: Open User Settings (JSON)
.
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "apiKey",
"description": "AgentQL API Key",
"password": true
}
],
"servers": {
"agentql": {
"command": "npx",
"args": [
"-y",
"agentql-mcp"
],
"env": {
"AGENTQL_API_KEY": "${input:apiKey}"
}
}
}
}
}
Optionally, you can add it to a file called .vscode/mcp.json
in your workspace. This will allow you to share the configuration with others.
{
"inputs": [
{
"type": "promptString",
"id": "apiKey",
"description": "AgentQL API Key",
"password": true
}
],
"servers": {
"agentql": {
"command": "npx",
"args": [
"-y",
"agentql-mcp"
],
"env": {
"AGENTQL_API_KEY": "${input:apiKey}"
}
}
}
}
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 insidemcpServers
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.