Firecrawl MCP Server

What does “Firecrawl” MCP Server do?

The Firecrawl MCP Server is a Model Context Protocol (MCP) implementation that empowers AI assistants with advanced web scraping and research capabilities. By integrating with the Firecrawl engine, this server allows AI clients to access and extract data from websites, perform deep research, execute batch scraping, and enable content discovery directly within development environments. Firecrawl MCP facilitates seamless access to up-to-date external information, supporting tasks such as content extraction, search, and automated research workflows. With features like automatic retries, rate limiting, and support for both cloud and self-hosted deployments, it significantly enhances the workflow of developers and LLM clients by making the web instantly accessible and actionable for AI agents.

List of Prompts

No specific prompt templates were found in the repository or documentation.

List of Resources

No explicit list of MCP “resources” found in the provided documentation or files.

List of Tools

• Web scraping: Enables AI clients to retrieve and parse content from web pages.
• Crawling and discovery: Allows the AI to traverse websites, identifying and collecting multiple resources.
• Search and content extraction: Supports searching for specific content within web pages and extracting relevant data.
• Deep research and batch scraping: Facilitates the collection of information from multiple sources in one operation.

Use Cases of this MCP Server

• Automated Web Research: Developers can automate the process of gathering information from multiple web sources to support technical research, market analysis, or literature reviews.
• Content Extraction: AI assistants can extract specific data (such as articles, tables, or contact info) from target websites for integration into workflows or databases.
• Competitive Intelligence: Teams can monitor competitors’ sites for updates, pricing changes, or new product launches by scraping relevant web pages on demand.
• Batch Data Gathering: Enables large-scale scraping of multiple URLs in one go, supporting data science, analytics, or training data collection.
• Integration with LLM Clients: Facilitates enhanced context for LLMs in environments like Cursor, Claude, or custom agents, allowing real-time access to the latest web data.

How to set it up

Windsurf

No specific instructions for Windsurf found.

Claude

No specific instructions for Claude found.

Cursor

1. Open Cursor Settings.
2. Navigate to Features > MCP Servers.
3. Click “+ Add new global MCP server”.
4. Enter the following JSON into the configuration panel:

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

5. Save settings and restart Cursor if required.

Note: Secure your API keys using environment variables as shown in the env field.

Cline

No specific instructions for Cline found.

Securing API Keys

API keys should be provided securely using environment variables. Example for Cursor:

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

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "firecrawl-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent will be able to use this MCP as a tool with access to all its functions and capabilities. Remember to replace "firecrawl-mcp" and the URL with your actual MCP server name and address.

Overview

| Roots Support | ⛔ (Not mentioned) |

Based on the above, Firecrawl MCP Server scores highly in tool functionality and setup clarity, but lacks explicit documentation on prompts, resources, roots, and sampling. Its large community (stars/forks) and open MIT license are strong positives. Overall, it is a well-supported MCP server for web scraping, but may require more documentation for advanced MCP capabilities.

Our opinion

The Firecrawl MCP Server provides a robust set of tools and easy setup for integrating powerful web scraping into LLM workflows. However, more documentation around prompts, resources, and advanced MCP features would enhance its usability for broader developer audiences.

MCP Score