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scrapegraph-mcp

一个生产就绪的模型上下文协议服务器,使语言模型能够利用人工智能驱动的网络爬虫功能,提供将网页转换为Markdown、提取结构化数据以及执行人工智能驱动的网络搜索的工具。

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README

ScrapeGraph MCP Server

ScapeGraph Smithery Integration ScrapeGraph Server MCP server MseeP.ai Security Assessment Badge

License: MIT Python 3.10 smithery badge

A production-ready Model Context Protocol (MCP) server that provides seamless integration with the ScapeGraph AI API. This server enables language models to leverage advanced AI-powered web scraping capabilities with enterprise-grade reliability.

Available Tools

The server provides the following enterprise-ready tools:

  • markdownify(website_url: str): Transform any webpage into clean, structured markdown format
  • smartscraper(user_prompt: str, website_url: str): Leverage AI to extract structured data from any webpage
  • searchscraper(user_prompt: str): Execute AI-powered web searches with structured, actionable results

Setup Instructions

To utilize this server, you'll need a ScapeGraph API key. Follow these steps to obtain one:

  1. Navigate to the ScapeGraph Dashboard
  2. Create an account and generate your API key

Automated Installation via Smithery

For automated installation of the ScrapeGraph API Integration Server using Smithery:

npx -y @smithery/cli install @ScrapeGraphAI/scrapegraph-mcp --client claude

Claude Desktop Configuration

Update your Claude Desktop configuration file with the following settings (located on the top rigth of the Cursor page):

(remember to add your API key inside the config)

{
    "mcpServers": {
        "@ScrapeGraphAI-scrapegraph-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "@smithery/cli@latest",
                "run",
                "@ScrapeGraphAI/scrapegraph-mcp",
                "--config",
                "\"{\\\"scrapegraphApiKey\\\":\\\"YOUR-SGAI-API-KEY\\\"}\""
            ]
        }
    }
}

The configuration file is located at:

  • Windows: %APPDATA%/Claude/claude_desktop_config.json
  • macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

Cursor Integration

Add the ScrapeGraphAI MCP server on the settings:

Cursor MCP Integration

Example Use Cases

The server enables sophisticated queries such as:

  • "Analyze and extract the main features of the ScapeGraph API"
  • "Generate a structured markdown version of the ScapeGraph homepage"
  • "Extract and analyze pricing information from the ScapeGraph website"
  • "Research and summarize recent developments in AI-powered web scraping"
  • "Create a comprehensive summary of the Python documentation website"

Error Handling

The server implements robust error handling with detailed, actionable error messages for:

  • API authentication issues
  • Malformed URL structures
  • Network connectivity failures
  • Rate limiting and quota management

Common Issues

Windows-Specific Connection

When running on Windows systems, you may need to use the following command to connect to the MCP server:

C:\Windows\System32\cmd.exe /c npx -y @smithery/cli@latest run @ScrapeGraphAI/scrapegraph-mcp --config "{\"scrapegraphApiKey\":\"YOUR-SGAI-API-KEY\"}"

This ensures proper execution in the Windows environment.

License

This project is distributed under the MIT License. For detailed terms and conditions, please refer to the LICENSE file.

Acknowledgments

Special thanks to tomekkorbak for his implementation of oura-mcp-server, which served as starting point for this repo.

Made with ❤️ by ScrapeGraphAI Team

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Runtime guide

cloud

Hosted runtime

Hosted servers run from a provider-managed environment. You usually connect the MCP client to the hosted endpoint or follow the provider's authorization flow, without keeping a local process alive

  1. Open provider connection page
  2. Authorize or copy endpoint
  3. Connect from your MCP client
terminal

Local runtime / other methods

Local servers run on your own machine or infrastructure. You normally copy the server_config into your MCP client, install the required package, and provide env variables from env_schema when needed

  1. Copy server_config
  2. Install required package
  3. Fill env variables and restart client