FireCrawl

FireCrawl Overview

Firecrawl MCP Server: Advanced Web Scraping for LLMs

What is Firecrawl MCP Server?

Firecrawl MCP Server is an official Model Context Protocol (MCP) server implementation that integrates with Firecrawl to provide powerful web scraping capabilities to Large Language Models (LLMs) such as Cursor, Claude, and other LLM clients. It allows LLMs to access and process web content for various tasks, acting as a bridge between LLMs and the web.

How to Use Firecrawl MCP Server?

Firecrawl MCP Server can be used by integrating it with your LLM client. Installation methods include:

Running with npx: env FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp
Manual Installation: npm install -g firecrawl-mcp
Integration with LLM Clients: Detailed configuration instructions are provided for Cursor (v0.45.6+ and v0.48.6+), Windsurf, Claude Desktop, and VS Code. This typically involves adding a JSON configuration block to your client's settings, specifying the command to run the Firecrawl MCP server and your Firecrawl API key.
SSE Local Mode: Run with env SSE_LOCAL=true FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp and access via http://localhost:3000/sse.
Smithery (Legacy): npx -y @smithery/cli install @mendableai/mcp-server-firecrawl --client claude

Configuration can be further customized using environment variables for retry behavior (e.g., FIRECRAWL_RETRY_MAX_ATTEMPTS, FIRECRAWL_RETRY_INITIAL_DELAY) and credit usage monitoring (e.g., FIRECRAWL_CREDIT_WARNING_THRESHOLD, FIRECRAWL_CREDIT_CRITICAL_THRESHOLD).

Key Features of Firecrawl MCP Server

Web Scraping, Crawling, and Discovery: Comprehensive tools for extracting data from websites.
Search and Content Extraction: Ability to search the web and extract relevant content.
Deep Research and Batch Scraping: Supports in-depth research and efficient processing of multiple URLs.
Automatic Retries and Rate Limiting: Handles transient errors and API rate limits automatically with exponential backoff.
Cloud and Self-Hosted Support: Flexible deployment options.
SSE Support: For Server-Sent Events.
Configurable Parameters: Customizable retry behavior and credit usage monitoring.
Robust Error Handling: Provides detailed error messages and network resilience.
Comprehensive Logging: Tracks operation status, performance, credit usage, and errors.

Use Cases of Firecrawl MCP Server

Firecrawl MCP Server provides a suite of tools for various web data extraction and research tasks:

firecrawl_scrape: Single page content extraction when the URL is known.
firecrawl_batch_scrape: Efficiently scrape content from multiple known URLs.
firecrawl_map: Discover all indexed URLs on a website.
firecrawl_search: Search the web for specific information and optionally extract content from results.
firecrawl_crawl: Asynchronously crawl a website to extract content from multiple related pages (use with caution due to potential token limits).
firecrawl_extract: Extract structured information from web pages using LLM capabilities based on a defined schema.
firecrawl_deep_research: Conduct in-depth web research on a query using intelligent crawling, search, and LLM analysis.
firecrawl_generate_llmstxt: Generate a standardized llms.txt file for a given domain, defining how LLMs should interact with the site.

FAQ from Firecrawl MCP Server

Q: How do I choose the right tool for my task? A: A quick reference table is provided in the documentation: * scrape: For single page content. * batch_scrape: For multiple known URLs. * map: For discovering URLs on a site. * crawl: For multi-page extraction (with limits). * search: For web search for information. * extract: For structured data from pages. * deep_research: For in-depth, multi-source research. * generate_llmstxt: For generating LLMs.txt for a domain.

Q: What are the common mistakes when using firecrawl_batch_scrape or firecrawl_crawl? A: For batch_scrape, using too many URLs at once may hit rate limits or token overflow. For crawl, setting limit or maxDepth too high can cause token overflow, and it's not recommended for single pages (use scrape instead).

Q: How does Firecrawl MCP Server handle rate limiting and batch processing? A: It utilizes Firecrawl's built-in capabilities, including automatic rate limit handling with exponential backoff, efficient parallel processing for batch operations, smart request queuing and throttling, and automatic retries for transient errors.

Q: What kind of logging and error handling does the server provide? A: The server includes comprehensive logging for operation status, performance, credit usage, rate limits, and errors. It also provides robust error handling with automatic retries, rate limit handling with backoff, detailed error messages, credit usage warnings, and network resilience.

FireCrawl's README

Firecrawl MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Firecrawl for web scraping capabilities.

Big thanks to @vrknetha, @knacklabs for the initial implementation!

Features

Web scraping, crawling, and discovery
Search and content extraction
Deep research and batch scraping
Automatic retries and rate limiting
Cloud and self-hosted support
SSE support

Play around with our MCP Server on MCP.so's playground or on Klavis AI.

Installation

Running with npx

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

Manual Installation

npm install -g firecrawl-mcp

Running on Cursor

Configuring Cursor 🖥️ Note: Requires Cursor version 0.45.6+ For the most up-to-date configuration instructions, please refer to the official Cursor documentation on configuring MCP servers: Cursor MCP Server Configuration Guide

To configure Firecrawl MCP in Cursor v0.48.6

Open Cursor Settings
Go to Features > MCP Servers
Click "+ Add new global MCP server"

Enter the following code:

{
  "mcpServers": {
    "firecrawl-mcp": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR-API-KEY"
      }
    }
  }
}

To configure Firecrawl MCP in Cursor v0.45.6

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

If you are using Windows and are running into issues, try cmd /c "set FIRECRAWL_API_KEY=your-api-key && npx -y firecrawl-mcp"

Replace your-api-key with your Firecrawl API key. If you don't have one yet, you can create an account and get it from https://www.firecrawl.dev/app/api-keys

After adding, refresh the MCP server list to see the new tools. The Composer Agent will automatically use Firecrawl MCP when appropriate, but you can explicitly request it by describing your web scraping needs. Access the Composer via Command+L (Mac), select "Agent" next to the submit button, and enter your query.

Running on Windsurf

Add this to your ./codeium/windsurf/model_config.json:

{
  "mcpServers": {
    "mcp-server-firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Running with SSE Local Mode

To run the server using Server-Sent Events (SSE) locally instead of the default stdio transport:

env SSE_LOCAL=true FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

Use the url: http://localhost:3000/sse

Installing via Smithery (Legacy)

To install Firecrawl for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @mendableai/mcp-server-firecrawl --client claude

Running on VS Code

For one-click installation, click one of the install buttons below...

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "apiKey",
        "description": "Firecrawl API Key",
        "password": true
      }
    ],
    "servers": {
      "firecrawl": {
        "command": "npx",
        "args": ["-y", "firecrawl-mcp"],
        "env": {
          "FIRECRAWL_API_KEY": "${input:apiKey}"
        }
      }
    }
  }
}

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "apiKey",
      "description": "Firecrawl API Key",
      "password": true
    }
  ],
  "servers": {
    "firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "${input:apiKey}"
      }
    }
  }
}

Configuration

Environment Variables

Required for Cloud API

FIRECRAWL_API_KEY: Your Firecrawl API key
- Required when using cloud API (default)
- Optional when using self-hosted instance with FIRECRAWL_API_URL
FIRECRAWL_API_URL (Optional): Custom API endpoint for self-hosted instances
- Example: https://firecrawl.your-domain.com
- If not provided, the cloud API will be used (requires API key)

Optional Configuration

Retry Configuration

FIRECRAWL_RETRY_MAX_ATTEMPTS: Maximum number of retry attempts (default: 3)
FIRECRAWL_RETRY_INITIAL_DELAY: Initial delay in milliseconds before 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)

Credit Usage Monitoring

FIRECRAWL_CREDIT_WARNING_THRESHOLD: Credit usage warning threshold (default: 1000)
FIRECRAWL_CREDIT_CRITICAL_THRESHOLD: Credit usage critical threshold (default: 100)

Configuration Examples

For cloud API usage with custom retry and credit monitoring:

# Required for cloud API
export FIRECRAWL_API_KEY=your-api-key

# Optional retry configuration
export FIRECRAWL_RETRY_MAX_ATTEMPTS=5        # Increase max retry attempts
export FIRECRAWL_RETRY_INITIAL_DELAY=2000    # Start with 2s delay
export FIRECRAWL_RETRY_MAX_DELAY=30000       # Maximum 30s delay
export FIRECRAWL_RETRY_BACKOFF_FACTOR=3      # More aggressive backoff

# Optional credit monitoring
export FIRECRAWL_CREDIT_WARNING_THRESHOLD=2000    # Warning at 2000 credits
export FIRECRAWL_CREDIT_CRITICAL_THRESHOLD=500    # Critical at 500 credits

For self-hosted instance:

# Required for self-hosted
export FIRECRAWL_API_URL=https://firecrawl.your-domain.com

# Optional authentication for self-hosted
export FIRECRAWL_API_KEY=your-api-key  # If your instance requires auth

# Custom retry configuration
export FIRECRAWL_RETRY_MAX_ATTEMPTS=10
export FIRECRAWL_RETRY_INITIAL_DELAY=500     # Start with faster retries

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR_API_KEY_HERE",

        "FIRECRAWL_RETRY_MAX_ATTEMPTS": "5",
        "FIRECRAWL_RETRY_INITIAL_DELAY": "2000",
        "FIRECRAWL_RETRY_MAX_DELAY": "30000",
        "FIRECRAWL_RETRY_BACKOFF_FACTOR": "3",

        "FIRECRAWL_CREDIT_WARNING_THRESHOLD": "2000",
        "FIRECRAWL_CREDIT_CRITICAL_THRESHOLD": "500"
      }
    }
  }
}

System Configuration

The server includes several configurable parameters that can be set via environment variables. Here are the default values if not configured:

const CONFIG = {
  retry: {
    maxAttempts: 3, // Number of retry attempts for rate-limited requests
    initialDelay: 1000, // Initial delay before first retry (in milliseconds)
    maxDelay: 10000, // Maximum delay between retries (in milliseconds)
    backoffFactor: 2, // Multiplier for exponential backoff
  },
  credit: {
    warningThreshold: 1000, // Warn when credit usage reaches this level
    criticalThreshold: 100, // Critical alert when credit usage reaches this level
  },
};

These configurations control:

Retry Behavior
- Automatically retries failed requests due to rate limits
- Uses exponential backoff to avoid overwhelming the API
- Example: With default settings, retries will be attempted at:
  - 1st retry: 1 second delay
  - 2nd retry: 2 seconds delay
  - 3rd retry: 4 seconds delay (capped at maxDelay)
Credit Usage Monitoring
- Tracks API credit consumption for cloud API usage
- Provides warnings at specified thresholds
- Helps prevent unexpected service interruption
- Example: With default settings:
  - Warning at 1000 credits remaining
  - Critical alert at 100 credits remaining

Rate Limiting and Batch Processing

The server utilizes Firecrawl's built-in rate limiting and batch processing capabilities:

Automatic rate limit handling with exponential backoff
Efficient parallel processing for batch operations
Smart request queuing and throttling
Automatic retries for transient errors

How to Choose a Tool

Use this guide to select the right tool for your task:

If you know the exact URL(s) you want:
- For one: use scrape
- For many: use batch_scrape
If you need to discover URLs on a site: use map
If you want to search the web for info: use search
If you want to extract structured data: use extract
If you want to analyze a whole site or section: use crawl (with limits!)
If you want to do in-depth research: use deep_research
If you want to generate LLMs.txt: use generate_llmstxt

Quick Reference Table

Tool	Best for	Returns
scrape	Single page content	markdown/html
batch_scrape	Multiple known URLs	markdown/html[]
map	Discovering URLs on a site	URL[]
crawl	Multi-page extraction (with limits)	markdown/html[]
search	Web search for info	results[]
extract	Structured data from pages	JSON
deep_research	In-depth, multi-source research	summary, sources
generate_llmstxt	LLMs.txt for a domain	text

Available Tools

1. Scrape Tool (`firecrawl_scrape`)

Scrape content from a single URL with advanced options.

Best for:

Single page content extraction, when you know exactly which page contains the information.

Not recommended for:

Extracting content from multiple pages (use batch_scrape for known URLs, or map + batch_scrape to discover URLs first, or crawl for full page content)
When you're unsure which page contains the information (use search)
When you need structured data (use extract)

Common mistakes:

Using scrape for a list of URLs (use batch_scrape instead).

Prompt Example:

"Get the content of the page at https://example.com."

Usage Example:

{
  "name": "firecrawl_scrape",
  "arguments": {
    "url": "https://example.com",
    "formats": ["markdown"],
    "onlyMainContent": true,
    "waitFor": 1000,
    "timeout": 30000,
    "mobile": false,
    "includeTags": ["article", "main"],
    "excludeTags": ["nav", "footer"],
    "skipTlsVerification": false
  }
}

Returns:

Markdown, HTML, or other formats as specified.

2. Batch Scrape Tool (`firecrawl_batch_scrape`)

Scrape multiple URLs efficiently with built-in rate limiting and parallel processing.

Best for:

Retrieving content from multiple pages, when you know exactly which pages to scrape.

Not recommended for:

Discovering URLs (use map first if you don't know the URLs)
Scraping a single page (use scrape)

Common mistakes:

Using batch_scrape with too many URLs at once (may hit rate limits or token overflow)

Prompt Example:

"Get the content of these three blog posts: [url1, url2, url3]."

Usage Example:

{
  "name": "firecrawl_batch_scrape",
  "arguments": {
    "urls": ["https://example1.com", "https://example2.com"],
    "options": {
      "formats": ["markdown"],
      "onlyMainContent": true
    }
  }
}

Returns:

Response includes operation ID for status checking:

{
  "content": [
    {
      "type": "text",
      "text": "Batch operation queued with ID: batch_1. Use firecrawl_check_batch_status to check progress."
    }
  ],
  "isError": false
}

3. Check Batch Status (`firecrawl_check_batch_status`)

Check the status of a batch operation.

{
  "name": "firecrawl_check_batch_status",
  "arguments": {
    "id": "batch_1"
  }
}

4. Map Tool (`firecrawl_map`)

Map a website to discover all indexed URLs on the site.

Best for:

Discovering URLs on a website before deciding what to scrape
Finding specific sections of a website

Not recommended for:

When you already know which specific URL you need (use scrape or batch_scrape)
When you need the content of the pages (use scrape after mapping)

Common mistakes:

Using crawl to discover URLs instead of map

Prompt Example:

"List all URLs on example.com."

Usage Example:

{
  "name": "firecrawl_map",
  "arguments": {
    "url": "https://example.com"
  }
}

Returns:

Array of URLs found on the site

5. Search Tool (`firecrawl_search`)

Search the web and optionally extract content from search results.

Best for:

Finding specific information across multiple websites, when you don't know which website has the information.
When you need the most relevant content for a query

Not recommended for:

When you already know which website to scrape (use scrape)
When you need comprehensive coverage of a single website (use map or crawl)

Common mistakes:

Using crawl or map for open-ended questions (use search instead)

Usage Example:

{
  "name": "firecrawl_search",
  "arguments": {
    "query": "latest AI research papers 2023",
    "limit": 5,
    "lang": "en",
    "country": "us",
    "scrapeOptions": {
      "formats": ["markdown"],
      "onlyMainContent": true
    }
  }
}

Returns:

Array of search results (with optional scraped content)

Prompt Example:

"Find the latest research papers on AI published in 2023."

6. Crawl Tool (`firecrawl_crawl`)

Starts an asynchronous crawl job on a website and extract content from all pages.

Best for:

Extracting content from multiple related pages, when you need comprehensive coverage.

Not recommended for:

Extracting content from a single page (use scrape)
When token limits are a concern (use map + batch_scrape)
When you need fast results (crawling can be slow)

Warning: Crawl responses can be very large and may exceed token limits. Limit the crawl depth and number of pages, or use map + batch_scrape for better control.

Common mistakes:

Setting limit or maxDepth too high (causes token overflow)
Using crawl for a single page (use scrape instead)

Prompt Example:

"Get all blog posts from the first two levels of example.com/blog."

Usage Example:

{
  "name": "firecrawl_crawl",
  "arguments": {
    "url": "https://example.com/blog/*",
    "maxDepth": 2,
    "limit": 100,
    "allowExternalLinks": false,
    "deduplicateSimilarURLs": true
  }
}

Returns:

Response includes operation ID for status checking:

{
  "content": [
    {
      "type": "text",
      "text": "Started crawl for: https://example.com/* with job ID: 550e8400-e29b-41d4-a716-446655440000. Use firecrawl_check_crawl_status to check progress."
    }
  ],
  "isError": false
}

7. Check Crawl Status (`firecrawl_check_crawl_status`)

Check the status of a crawl job.

{
  "name": "firecrawl_check_crawl_status",
  "arguments": {
    "id": "550e8400-e29b-41d4-a716-446655440000"
  }
}

Returns:

Response includes the status of the crawl job:

8. Extract Tool (`firecrawl_extract`)

Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction.

Best for:

Extracting specific structured data like prices, names, details.

Not recommended for:

When you need the full content of a page (use scrape)
When you're not looking for specific structured data

Arguments:

urls: Array of URLs to extract information from
prompt: Custom prompt for the LLM extraction
systemPrompt: System prompt to guide the LLM
schema: JSON schema for structured data extraction
allowExternalLinks: Allow extraction from external links
enableWebSearch: Enable web search for additional context
includeSubdomains: Include subdomains in extraction

When using a self-hosted instance, the extraction will use your configured LLM. For cloud API, it uses Firecrawl's managed LLM service. Prompt Example:

"Extract the product name, price, and description from these product pages."

Usage Example:

{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://example.com/page1", "https://example.com/page2"],
    "prompt": "Extract product information including name, price, and description",
    "systemPrompt": "You are a helpful assistant that extracts product information",
    "schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "price": { "type": "number" },
        "description": { "type": "string" }
      },
      "required": ["name", "price"]
    },
    "allowExternalLinks": false,
    "enableWebSearch": false,
    "includeSubdomains": false
  }
}

Returns:

Extracted structured data as defined by your schema

{
  "content": [
    {
      "type": "text",
      "text": {
        "name": "Example Product",
        "price": 99.99,
        "description": "This is an example product description"
      }
    }
  ],
  "isError": false
}

9. Deep Research Tool (`firecrawl_deep_research`)

Conduct deep web research on a query using intelligent crawling, search, and LLM analysis.

Best for:

Complex research questions requiring multiple sources, in-depth analysis.

Not recommended for:

Simple questions that can be answered with a single search
When you need very specific information from a known page (use scrape)
When you need results quickly (deep research can take time)

Arguments:

query (string, required): The research question or topic to explore.
maxDepth (number, optional): Maximum recursive depth for crawling/search (default: 3).
timeLimit (number, optional): Time limit in seconds for the research session (default: 120).
maxUrls (number, optional): Maximum number of URLs to analyze (default: 50).

Prompt Example:

"Research the environmental impact of electric vehicles versus gasoline vehicles."

Usage Example:

{
  "name": "firecrawl_deep_research",
  "arguments": {
    "query": "What are the environmental impacts of electric vehicles compared to gasoline vehicles?",
    "maxDepth": 3,
    "timeLimit": 120,
    "maxUrls": 50
  }
}

Returns:

Final analysis generated by an LLM based on research. (data.finalAnalysis)
May also include structured activities and sources used in the research process.

10. Generate LLMs.txt Tool (`firecrawl_generate_llmstxt`)

Generate a standardized llms.txt (and optionally llms-full.txt) file for a given domain. This file defines how large language models should interact with the site.

Best for:

Creating machine-readable permission guidelines for AI models.

Not recommended for:

General content extraction or research

Arguments:

url (string, required): The base URL of the website to analyze.
maxUrls (number, optional): Max number of URLs to include (default: 10).
showFullText (boolean, optional): Whether to include llms-full.txt contents in the response.

Prompt Example:

"Generate an LLMs.txt file for example.com."

Usage Example:

{
  "name": "firecrawl_generate_llmstxt",
  "arguments": {
    "url": "https://example.com",
    "maxUrls": 20,
    "showFullText": true
  }
}

Returns:

LLMs.txt file contents (and optionally llms-full.txt)

Logging System

The server includes comprehensive logging:

Operation status and progress
Performance metrics
Credit usage monitoring
Rate limit tracking
Error conditions

Example log messages:

[INFO] Firecrawl MCP Server initialized successfully
[INFO] Starting scrape for URL: https://example.com
[INFO] Batch operation queued with ID: batch_1
[WARNING] Credit usage has reached warning threshold
[ERROR] Rate limit exceeded, retrying in 2s...

Error Handling

The server provides robust error handling:

Automatic retries for transient errors
Rate limit handling with backoff
Detailed error messages
Credit usage warnings
Network resilience

Example error response:

{
  "content": [
    {
      "type": "text",
      "text": "Error: Rate limit exceeded. Retrying in 2 seconds..."
    }
  ],
  "isError": true
}

Development

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

Contributing

Fork the repository
Create your feature branch
Run tests: npm test
Submit a pull request

Thanks to contributors

Thanks to @vrknetha, @cawstudios for the initial implementation!

Thanks to MCP.so and Klavis AI for hosting and @gstarwd, @xiangkaiz and @zihaolin96 for integrating our server.

License

MIT License - see LICENSE file for details

FireCrawl Reviews

Reviews feature coming soon

Stay tuned for community discussions and feedback

FireCrawl Overview

Firecrawl MCP Server: Advanced Web Scraping for LLMs

What is Firecrawl MCP Server?

How to Use Firecrawl MCP Server?

Key Features of Firecrawl MCP Server

Use Cases of Firecrawl MCP Server

FAQ from Firecrawl MCP Server

FireCrawl's README

Firecrawl MCP Server

Features

Installation

Running with npx

Manual Installation

Running on Cursor

Running on Windsurf

Running with SSE Local Mode

Installing via Smithery (Legacy)

Running on VS Code

Configuration

Environment Variables

Required for Cloud API

Optional Configuration

Configuration Examples

Usage with Claude Desktop

System Configuration

Rate Limiting and Batch Processing

How to Choose a Tool

Quick Reference Table

Available Tools

1. Scrape Tool (firecrawl_scrape)

2. Batch Scrape Tool (firecrawl_batch_scrape)

3. Check Batch Status (firecrawl_check_batch_status)

4. Map Tool (firecrawl_map)

5. Search Tool (firecrawl_search)

6. Crawl Tool (firecrawl_crawl)

7. Check Crawl Status (firecrawl_check_crawl_status)

8. Extract Tool (firecrawl_extract)

9. Deep Research Tool (firecrawl_deep_research)

10. Generate LLMs.txt Tool (firecrawl_generate_llmstxt)

Logging System

Error Handling

Development

Contributing

Thanks to contributors

License

FireCrawl Reviews

Actions

FireCrawl's Information

1. Scrape Tool (`firecrawl_scrape`)

2. Batch Scrape Tool (`firecrawl_batch_scrape`)

3. Check Batch Status (`firecrawl_check_batch_status`)

4. Map Tool (`firecrawl_map`)

5. Search Tool (`firecrawl_search`)

6. Crawl Tool (`firecrawl_crawl`)

7. Check Crawl Status (`firecrawl_check_crawl_status`)

8. Extract Tool (`firecrawl_extract`)

9. Deep Research Tool (`firecrawl_deep_research`)

10. Generate LLMs.txt Tool (`firecrawl_generate_llmstxt`)