What is MCP (Model Context Protocol)?

MCP is an open protocol that standardizes how applications provide context to LLMs, enabling seamless integration between AI models and external data sources.

What are the key features of Firecrawl MCP Server?

Key features include web scraping with JS rendering, URL discovery and crawling, web search with content extraction, efficient batch processing with rate limiting, credit usage monitoring, and comprehensive logging.

How do I install Firecrawl MCP Server?

You can install it using `npx` or `npm`. See the installation instructions in the documentation for detailed steps.

What environment variables do I need to configure?

You need to set the `FIRECRAWL_API_KEY` for cloud API usage. Optional environment variables include `FIRECRAWL_API_URL` for self-hosted instances and variables for retry configuration and credit monitoring.

How do I use Firecrawl MCP Server with Cursor?

Configure Firecrawl MCP in Cursor settings by adding a new MCP server with the appropriate command and API key.

Can I use Firecrawl MCP Server with Claude Desktop?

Yes, you can configure it in the `claude_desktop_config.json` file with the necessary command, arguments, and environment variables.

What is the purpose of the `llms.txt` file?

The `llms.txt` file provides instructions for how large language models should interact with the website, promoting responsible and informed usage.

What is UBOS and how does it relate to Firecrawl MCP Server?

UBOS is a full-stack AI Agent Development Platform. The Firecrawl MCP Server is available on the UBOS Asset Marketplace, offering seamless integration for UBOS users.

What are some example use cases for Firecrawl MCP Server?

Use cases include market research, lead generation, content aggregation, financial analysis, and scientific research.

How does Firecrawl MCP Server handle rate limiting?

It utilizes Firecrawl's built-in rate limiting and implements exponential backoff for automatic retries, ensuring efficient and reliable data extraction.

What kind of error handling is included?

The server provides robust error handling, including automatic retries, rate limit handling with backoff, detailed error messages, and credit usage warnings.

How can I monitor credit usage?

The server includes credit usage monitoring with configurable warning and critical thresholds, helping you prevent unexpected service interruptions.

What tools are available with Firecrawl MCP Server?

Available tools include `firecrawl_scrape`, `firecrawl_batch_scrape`, `firecrawl_check_batch_status`, `firecrawl_search`, `firecrawl_crawl`, `firecrawl_extract`, `firecrawl_deep_research` and `firecrawl_generate_llmstxt` each designed for specific web scraping and data extraction tasks.

What does the Deep Research Tool do?

The Deep Research Tool conducts web research using intelligent crawling, search, and LLM analysis. You provide a query, and it returns a final analysis generated by an LLM based on the research.

Firecrawl MCP Server – Overview

Q: What is Firecrawl MCP Server?

Firecrawl MCP Server is an implementation of the Model Context Protocol (MCP) that integrates with Firecrawl for powerful web scraping capabilities, allowing LLMs to access and interact with web data.

Unleash the Power of Web Data with Firecrawl MCP Server on UBOS

In the rapidly evolving landscape of AI and Large Language Models (LLMs), access to timely and relevant data is paramount. The Firecrawl MCP (Model Context Protocol) Server, now available on the UBOS Asset Marketplace, provides a robust solution for integrating powerful web scraping capabilities directly into your AI workflows. This integration unlocks a wealth of opportunities for enhancing the intelligence and effectiveness of AI agents, making it an indispensable tool for developers and businesses alike.

What is Firecrawl MCP Server?

At its core, the Firecrawl MCP Server acts as a bridge between LLMs and the vast expanse of the internet. It’s a specialized server implementation that leverages the capabilities of Firecrawl, a powerful web scraping library, to provide LLMs with real-time access to web data. This enables AI models to:

Scrape and Crawl Websites: Extract structured and unstructured data from websites, even those with dynamic content rendered by JavaScript.
Perform Web Searches: Integrate web search results directly into AI workflows, allowing models to gather information on demand.
Extract Key Information: Identify and extract specific data points from web pages based on predefined criteria.
Conduct Deep Research: Automate comprehensive research tasks by combining crawling, searching, and LLM-powered analysis.

The MCP (Model Context Protocol) itself standardizes how applications provide context to LLMs. By adhering to this protocol, Firecrawl MCP Server ensures seamless integration with various LLM clients and platforms.

Key Features and Benefits

Advanced Web Scraping: Firecrawl MCP Server goes beyond basic HTML parsing. It supports JavaScript rendering, allowing you to scrape even the most dynamic websites. This ensures that your AI models have access to the full richness of web data.
Intelligent Crawling: The server can automatically discover and crawl websites, following links and extracting data from multiple pages. This is invaluable for gathering comprehensive datasets and conducting in-depth research.
Efficient Batch Processing: Firecrawl MCP Server is designed for efficient batch processing of multiple URLs. It includes built-in rate limiting to prevent overloading websites and ensure reliable data extraction.
Web Search Integration: The server can perform web searches and extract content from search results. This enables AI models to gather information on demand and stay up-to-date with the latest developments.
Customizable Extraction: The extract tool allows you to extract structured information from web pages using LLM capabilities. You can define custom prompts, system prompts, and JSON schemas to tailor the extraction process to your specific needs.
Deep Research Capabilities: The deep research tool allows you to conduct comprehensive web research on a query using intelligent crawling, search, and LLM analysis. This is ideal for automating complex research tasks and gaining deeper insights into specific topics.
Credit Usage Monitoring: For cloud API usage, the server provides comprehensive credit usage monitoring. This helps you track your API consumption and prevent unexpected service interruptions.
Robust Error Handling: The server includes robust error handling mechanisms, including automatic retries for transient errors and rate limit handling with exponential backoff. This ensures that your AI workflows are resilient and reliable.
Comprehensive Logging: The server provides detailed logging of operation status, performance metrics, credit usage, rate limit tracking, and error conditions. This makes it easy to monitor and troubleshoot your AI workflows.
llms.txt Generation: The server can generate llms.txt files for specified domains, which offer instructions for how large language models should interact with the site.

Use Cases

The Firecrawl MCP Server opens up a wide range of use cases for AI-powered applications, including:

Market Research: Gather data on competitor pricing, product features, and customer sentiment to inform your business strategy.
Lead Generation: Identify potential leads by scraping websites and extracting contact information.
Content Aggregation: Build content aggregation platforms by scraping news articles, blog posts, and other online content.
Financial Analysis: Track stock prices, financial news, and economic indicators to make informed investment decisions.
Scientific Research: Gather data from scientific publications, research databases, and other online resources to accelerate scientific discovery.
E-commerce Monitoring: Track product availability, pricing changes, and customer reviews on e-commerce websites.
Sentiment Analysis: Analyze social media posts, reviews, and other online content to gauge public opinion on products, services, and brands.
News Monitoring: Track news articles and other online content related to specific topics or events.
Real-time Data Integration: Integrate real-time web data into AI models for applications such as fraud detection, risk management, and personalized recommendations.

Getting Started with Firecrawl MCP Server on UBOS

Integrating Firecrawl MCP Server into your AI workflows is straightforward. The server can be easily installed and configured through the UBOS Asset Marketplace.

Installation

You can install the Firecrawl MCP Server using npx or by manually installing it using npm:

Running with npx:

bash env FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

Manual Installation:

bash npm install -g firecrawl-mcp

Configuration

The server can be configured using environment variables. The following environment variables are required for cloud API usage:

FIRECRAWL_API_KEY: Your Firecrawl API key.

The following environment variables are optional:

FIRECRAWL_API_URL: Custom API endpoint for self-hosted instances.
FIRECRAWL_RETRY_MAX_ATTEMPTS: Maximum number of retry attempts (default: 3).
FIRECRAWL_RETRY_INITIAL_DELAY: Initial delay in milliseconds before the first retry (default: 1000).
FIRECRAWL_RETRY_MAX_DELAY: Maximum delay in milliseconds between retries (default: 10000).
FIRECRAWL_RETRY_BACKOFF_FACTOR: Exponential backoff multiplier (default: 2).
FIRECRAWL_CREDIT_WARNING_THRESHOLD: Credit usage warning threshold (default: 1000).
FIRECRAWL_CREDIT_CRITICAL_THRESHOLD: Credit usage critical threshold (default: 100).

Usage with LLM Clients

Once the server is installed and configured, it can be used with various LLM clients, including Cursor, Claude, and Windsurf. The integration process varies depending on the client, but typically involves adding the server’s command and arguments to the client’s configuration file.

For example, to configure Firecrawl MCP in Cursor:

Open Cursor Settings.
Go to Features > MCP Servers.
Click “+ Add New MCP Server”.
Enter the following:
- Name: “firecrawl-mcp” (or your preferred name).
- Type: “command”.
- Command: env FIRECRAWL_API_KEY=your-api-key npx -y firecrawl-mcp

UBOS: Your Full-Stack AI Agent Development Platform

UBOS is a comprehensive platform designed to empower businesses in building and deploying AI agents across various departments. Beyond offering tools like the Firecrawl MCP Server, UBOS provides a complete ecosystem for AI agent development, orchestration, and integration with enterprise data. With UBOS, you can:

Orchestrate AI Agents: Manage and coordinate multiple AI agents to work together seamlessly.
Connect to Enterprise Data: Integrate AI agents with your existing data sources, including databases, APIs, and file systems.
Build Custom AI Agents: Develop custom AI agents tailored to your specific needs using your own LLM models.
Create Multi-Agent Systems: Design and deploy complex AI systems that combine the strengths of multiple agents.

By leveraging the UBOS platform, businesses can unlock the full potential of AI and transform their operations.

Conclusion

The Firecrawl MCP Server on the UBOS Asset Marketplace provides a powerful and versatile solution for integrating web scraping capabilities into your AI workflows. With its advanced features, robust error handling, and seamless integration with LLM clients, this server is an indispensable tool for developers and businesses looking to leverage the vast wealth of data available on the web. Combined with the comprehensive capabilities of the UBOS platform, the Firecrawl MCP Server empowers you to build intelligent, data-driven AI applications that can transform your business.