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Documentation Index

Fetch the complete documentation index at: https://docs.scrapegraphai.com/llms.txt

Use this file to discover all available pages before exploring further.

Overview

The ScrapeGraphAI MCP Server is a production-ready Model Context Protocol (MCP) server that connects Large Language Models (LLMs) to the ScrapeGraph AI API. It enables AI assistants like Claude and Cursor to perform AI-powered web scraping, research, and crawling directly through natural language interactions.

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What is MCP?

The Model Context Protocol (MCP) is a standardized way for AI assistants to access external tools and data sources. By using the ScrapeGraphAI MCP Server, your AI assistant gains access to powerful web scraping capabilities without needing to write code.

Key Features

17 Powerful Tools

Scrape, extract, search, crawl, generate schemas, monitor scheduled jobs (with activity polling), and manage your account

Remote & Local

Use the hosted HTTP endpoint or run locally via Python

Universal Compatibility

Works with Cursor, Claude Desktop, and any MCP-compatible client

Production Ready

Robust error handling, timeouts, and reliability tested in production

Available Tools

The MCP server exposes the following tools via API v2:
ToolDescription
scrapeFetch page content in any format: markdown (default), html, screenshot, branding, links, images, summary (POST /scrape)
extractAI-powered structured extraction from a URL (POST /extract)
searchSearch the web and extract structured results (POST /search)
crawl_startStart async multi-page crawl β€” markdown, html, links, images, summary, branding, or screenshot (POST /crawl)
crawl_get_statusPoll crawl results (GET /crawl/:id)
crawl_stopStop a running crawl job (POST /crawl/:id/stop)
crawl_resumeResume a stopped crawl job (POST /crawl/:id/resume)
schemaGenerate or augment a JSON Schema from a prompt (POST /schema)
creditsCheck your credit balance (GET /credits)
historyBrowse request history with pagination (GET /history)
monitor_createCreate a scheduled extraction job (POST /monitor)
monitor_listList all monitors (GET /monitor)
monitor_getGet monitor details (GET /monitor/:id)
monitor_pausePause a running monitor (POST /monitor/:id/pause)
monitor_resumeResume a paused monitor (POST /monitor/:id/resume)
monitor_deleteDelete a monitor (DELETE /monitor/:id)
monitor_activityPoll tick history for a monitor with pagination (GET /monitor/:id/activity)
Migrating from v2 (scrapegraph-mcp ≀ 2.x)? Tools were renamed in v3.0.0 to match the v2 API canonical names: smartscraper β†’ extract, searchscraper β†’ search, smartcrawler_initiate β†’ crawl_start, smartcrawler_fetch_results β†’ crawl_get_status, sgai_history β†’ history, generate_schema β†’ schema. markdownify was removed β€” use scrape with output_format="markdown" instead. See the v3.0.0 release notes for full details.

Quick Start

1

Get Your API Key

Create an account and copy your API key from the ScrapeGraph Dashboard
2

Choose Your Client

Select your preferred AI assistant: Cursor or Claude Desktop
3

Configure MCP

Follow the setup guide for your client to connect the MCP server
4

Start Scraping

Ask your AI assistant to scrape websites, extract data, or perform research

Setup Guides

https://mintcdn.com/scrapegraphaiinc-9e950277/3eC85GCQlhpZd6A9/logo/APP_ICON_2D_DARK.png?fit=max&auto=format&n=3eC85GCQlhpZd6A9&q=85&s=60fd0bda96a73202515632f745e72893

Cursor Setup

Configure ScrapeGraph MCP in Cursor (remote-first)
https://mintcdn.com/scrapegraphaiinc-9e950277/PRteAQT1vZQ6-ik0/logo/claude-color.svg?fit=max&auto=format&n=PRteAQT1vZQ6-ik0&q=85&s=4a3fc52a53394c0f568a6171c3ad3a32

Claude Desktop Setup

Configure ScrapeGraph MCP in Claude Desktop (remote-first)
The easiest way to get started is using our hosted MCP endpoint:
https://mcp.scrapegraphai.com/mcp
This requires no local installation and works seamlessly with both Cursor and Claude Desktop. See the setup guides above for configuration details.

Local Installation

Prefer running locally? You can install the Python package and run it via stdio. This gives you more control and doesn’t require internet connectivity for the MCP connection itself.
The remote endpoint is recommended for most users as it’s simpler to set up and maintain.

Use Cases

  • Research & Analysis - Extract data from multiple sources for research
  • Content Aggregation - Collect and structure content from websites
  • Market Intelligence - Monitor competitors and market trends
  • Lead Generation - Extract contact information and company data
  • Data Collection - Build datasets from web sources

Next Steps

  • Read the detailed setup guide for Cursor
  • Read the detailed setup guide for Claude Desktop
  • Browse the GitHub repo for source, advanced configuration, and release notes

Ready to Start?

Choose your client and start scraping with AI!