What configuration is required?
You must set the `WEBSCRAPING_AI_API_KEY` environment variable. Optional settings include concurrency limit, proxy type, JavaScript rendering, and content sandboxing.
@webscraping-ai
基本信息
A Model Context Protocol (MCP) server implementation that integrates with WebScraping.AI for web data extraction capabilities.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"WebScraping.AI": {
"command": "npx",
"args": [
"-y",
"webscraping-ai-mcp"
],
"env": {
"WEBSCRAPING_AI_API_KEY": "test-api-key"
}
}
}
}A Model Context Protocol (MCP) server that integrates with WebScraping.AI to provide web data extraction capabilities for LLM platforms like Claude. It enables AI agents to scrape, extract, and answer questions from web page content.
Run with npx -y webscraping-ai-mcp after setting the required WEBSCRAPING_AI_API_KEY environment variable. Configure in Cursor (.cursor/mcp.json) or Claude Desktop (claude_desktop_config.json) with optional settings like WEBSCRAPING_AI_CONCURRENCY_LIMIT (default 5) and WEBSCRAPING_AI_ENABLE_CONTENT_SANDBOXING. Tools are invoked via MCP tool calls.
You must set the `WEBSCRAPING_AI_API_KEY` environment variable. Optional settings include concurrency limit, proxy type, JavaScript rendering, and content sandboxing.
When enabled (`WEBSCRAPING_AI_ENABLE_CONTENT_SANDBOXING=true`), scraped content is wrapped with security boundaries and a warning not to execute commands from that section, helping protect LLMs against indirect prompt injection.
Three proxy types: `datacenter`, `residential`, and `stealth`. Residential is the default. Stealth is recommended for heavily protected sites but costs more.
It provides automatic retries for transient errors, rate limit handling with backoff, and returns detailed error messages (e.g., HTTP 429).
The server connects via STDIO transport, configured as a command in MCP‑enabled platforms. Authentication is done through the API key passed as an environment variable.
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