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Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients

The Official Firecrawl MCP Server is designed to seamlessly integrate powerful web scraping capabilities into large language model clients like Cursor, Claude, and others, enabling them to access and process real-time web content.

JavaScript
Added on 2025年6月6日
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Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients preview
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Project Introduction

Summary

This project is an open-source server implementation of the Firecrawl MCP protocol, enabling large language models (LLMs) to perform web scraping and retrieve up-to-date information from the internet.

Problem Solved

LLMs are often limited by their training data's cutoff point and inability to interact with the live web. This server provides a bridge, allowing LLMs to access current, dynamic information directly from the internet.

Core Features

Web Scraping API

Provides a robust API endpoint that LLM clients can call to request web pages and receive processed content.

LLM Client Integration

Designed to work out-of-the-box with popular LLM clients such as Cursor and Claude, with support for integrating others.

Content Extraction

Extracts relevant text, links, and potentially other structured data from web pages based on client requests.

Scalable Architecture

Built to handle multiple concurrent requests efficiently, providing timely responses to connected clients.

Tech Stack

Node.js
Docker
Express.js
Queue System (e.g., BullMQ/Redis)

使用场景

The server enables a variety of applications where LLMs require up-to-date or specific web content:

场景一:实时信息获取

Details

An LLM can scrape a news website or financial portal to get the latest headlines or stock prices during a user query.

User Value

Provides LLMs with current data, overcoming limitations of static training sets.

场景二:特定内容研究与总结

Details

An LLM client helps a user research a topic by scraping and summarizing content from specific URLs (e.g., documentation pages, research papers).

User Value

Enables LLMs to process and synthesize information from user-specified online sources.

场景三:网页数据提取与交互

Details

An LLM needs to interact with a website to retrieve data that requires basic navigation or input.

User Value

Extends LLM capabilities beyond simple text analysis to interacting with web interfaces.

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