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ScrapeGraphAI Scrapegraph MCP

Connects MCP-compatible AI clients to ScrapeGraph AI for web scraping, extraction, search, crawling, schemas, and account data.

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Install ScrapeGraphAI Scrapegraph MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "scrapegraphai-scrapegraph-mcp": {
      "command": "uvx",
      "args": [
        "scrapegraph-mcp"
      ],
      "env": {
        "SGAI_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 8 tools correctly (18d ago).

Overview

ScrapeGraphAI-scrapegraph-mcp MCP server connects MCP-compatible clients to the ScrapeGraph API v2 for AI-assisted web scraping and data extraction. It provides tools for scraping pages into several formats, extracting prompted data, searching the web, starting asynchronous crawls, generating schemas, and viewing account or activity data. Crawls return an identifier that can be checked until completion, while search and extraction requests accept options such as prompts, schemas, country, and time range. Use it when an agent needs structured information from websites through ScrapeGraph AI rather than direct browser automation.

Use cases

  • Scrape web pages into markdown, HTML, screenshots, or summaries
  • Extract product or page data with prompts and optional schemas
  • Search the web with country and time-range filters
  • Crawl multiple pages and poll for completion
  • Generate JSON Schemas for structured extraction

Key features

  • Multi-format web scraping
  • Prompt-based structured extraction
  • Web search with filters
  • Asynchronous crawling
  • JSON Schema generation
  • Paginated history and activity data

Tools8

What ScrapeGraphAI Scrapegraph MCP lets your agent do. Expand a tool for its parameters.

Verified live~7.1k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

27/27 checks healthy over the last 91d

What it can do

From the 8 tools this server returned when we ran it; 8 declare their own safety hints.

Runs commands · 1Makes network requests · 6Read-only tools · 7
Network at startup2 connections
Files written at startup11

Observed 2026-10-22 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

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Scanned 3d ago via OSV.dev · scrapegraph-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars121Stargazers on the source repository.
Last commit3mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed8Callable tools this server registers over MCP.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How ScrapeGraphAI Scrapegraph MCP works

What ScrapeGraphAI-scrapegraph-mcp does

ScrapeGraphAI-scrapegraph-mcp exposes ScrapeGraph AI API v2 through the Model Context Protocol. It is intended for agents that need to retrieve and transform web content without implementing direct HTTP calls to the ScrapeGraph service themselves.

The server supports single-page scraping, prompt-based extraction, web search, asynchronous multi-page crawling, JSON Schema generation, and account-related queries. Scraping can return markdown, HTML, screenshots, branding information, links, images, or summaries. Extraction takes a website URL and a user prompt, with an optional output schema for structured results.

How it works

Requests are forwarded to the ScrapeGraph API v2 base URL, which defaults to https://v2-api.scrapegraphai.com/api. Authentication uses the SGAI-APIKEY header. An API key can be supplied through the environment or through the MCP configuration mechanisms described by the project.

The search tool accepts a result count from 1 to 20 and can use country, time-range, and schema options. For larger jobs, crawl_start begins an asynchronous crawl and returns an identifier. Call crawl_get_status with that identifier and continue polling until the response reports status: completed.

The schema tool can create or extend a JSON Schema from a prompt. history provides paginated request history with an optional service filter, while monitor_activity returns paginated activity records containing fields such as status, change information, elapsed time, and diffs.

Setup and configuration

ScrapeGraphAI-scrapegraph-mcp requires a ScrapeGraph API key, available from the ScrapeGraph Dashboard. The repository supports Python 3.13 or later and installation with either pip or uv. After installing the package, run scrapegraph-mcp or python -m scrapegraph_mcp.server; local communication uses standard input and output.

The primary environment variables are:

  • SGAI_API_KEY: API key used to authenticate with ScrapeGraph.
  • SGAI_API_URL: optional replacement for the default API base URL.
  • SGAI_TIMEOUT: optional request timeout in seconds; the documented default is 120.

The older names SCRAPEGRAPH_API_BASE_URL and SGAI_TIMEOUT_S remain supported as aliases. Smithery installation is documented for Claude Desktop, and the repository also provides local and remote configuration examples for Claude Desktop and Cursor. The hosted endpoint documented for new integrations is https://sgai-mcp-main.onrender.com; the older mcp.scrapegraphai.com/mcp endpoint is marked for deprecation.

Tools and capabilities

  • scrape: retrieves a page in a selected output format.
  • extract: extracts information from a URL using a user prompt and optional schema.
  • search: searches for web results with filtering and schema options.
  • crawl_start: starts a crawl with a selected extraction mode.
  • crawl_get_status: checks the status of a crawl by ID.
  • schema: generates or augments a JSON Schema.
  • credits: retrieves account credit information.
  • history: lists paginated service history.
  • monitor_activity: lists paginated activity ticks for a monitor.

Limitations and notes

The server targets ScrapeGraph API v2, so requests depend on that service, its API key, and available account credits. Crawl operations are asynchronous and require status polling. The repository documents the removal of older names and features, including markdownify, sitemap, and agentic_scrapper; markdown output should be requested through scrape instead. ScrapeGraphAI-scrapegraph-mcp does not replace the underlying API's access, rate, or service requirements.

Read the full README View source on GitHub

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Frequently Asked Questions about ScrapeGraphAI Scrapegraph MCP

ScrapeGraphAI-scrapegraph-mcp is an MCP server that connects AI clients to the ScrapeGraph API v2. Its main tools scrape and extract website data, search the web, start and check crawls, generate JSON Schemas, and retrieve credits, history, and monitor activity through MCP-compatible interfaces.

Install it locally with Python 3.13 or later using pip or uv, or use the documented Smithery command for Claude Desktop. Local execution uses `scrapegraph-mcp` or `python -m scrapegraph_mcp.server`, and requires a ScrapeGraph API key.

Yes, ScrapeGraphAI-scrapegraph-mcp works with Cursor.

Set `SGAI_API_KEY` to your ScrapeGraph API key before starting the local server. The key can also be passed through MCP configuration as `scrapegraphApiKey` or through the documented remote HTTP `X-API-Key` header.

Call `crawl_get_status` with the identifier returned by `crawl_start`, then poll until the response contains `status: completed`. Crawl jobs run asynchronously, so the initial start request does not provide the final results immediately.

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