Developers & MCP

Updated Feb 2026

Integrate Firecrawl directly into your AI development environment using the Model Context Protocol (MCP). Give Claude Desktop, Cursor, and other AI coding assistants real-time access to documentation, APIs, and web resources without leaving your editor.

Why Firecrawl for Developers

AI coding assistants are powerful but limited by their training data. When you ask about a new API, a recent library update, or current documentation, the assistant may hallucinate outdated information. Firecrawl's MCP server gives your AI assistant live access to the web so it can look things up in real time.

Key Benefits

• Real-time documentation access -- Your AI assistant reads current docs instead of guessing from stale training data
• Zero infrastructure -- No servers to manage, no crawlers to maintain; configure once and go
• Automatic caching -- MCP server caches responses for 15 minutes (configurable) to avoid redundant requests
• Standard rate limits -- MCP requests use your existing Firecrawl API rate limits

How It Works

The MCP server acts as a bridge between your AI assistant and the web. When you ask your assistant to look up documentation or check a website, it calls the Firecrawl MCP server, which extracts the page content and returns clean, structured data back to the assistant.

Firecrawl Features Used

Feature	Role for Developers
MCP Server	Bridge between AI assistants and Firecrawl's extraction API
Scrape	Extract documentation pages, API references, and code examples
Search	Find relevant documentation and Stack Overflow answers
Map	Discover all pages in a documentation site
Crawl	Ingest entire documentation sites for comprehensive context

Setting Up the MCP Server

Claude Desktop

Add Firecrawl to your Claude Desktop MCP configuration:

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

Cursor

Configure the MCP server in Cursor's settings to enable web scraping directly from your editor:

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

Claude Code

Add the Firecrawl MCP server to your .mcp.json configuration:

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

Once configured, your AI assistant can access Firecrawl's tools -- scrape, search, crawl, map, and extract -- directly through the MCP protocol.

Use Case: Live Documentation Lookup

Once MCP is configured, you can ask your AI assistant questions like:

• "Scrape the Next.js 15 migration guide and help me update my app"
• "Search for the latest Prisma schema syntax changes"
• "Crawl the Stripe API docs and find all webhook event types"
• "Check the React Router v7 docs for the new data loading pattern"

The assistant fetches live content through Firecrawl instead of relying on potentially outdated training data.

Use Case: Build Applications from Website Content

The Open Lovable template enables developers to build complete applications directly from website content:

1. Point Firecrawl at an existing website
2. Extract its content, structure, and design patterns
3. Generate a modern React application based on the extracted data
4. Iterate using your AI assistant with live access to the source

Programmatic Integration

Beyond MCP, developers can integrate Firecrawl directly into their applications:

Python SDK

from firecrawl import Firecrawl

app = Firecrawl(api_key="fc-YOUR_API_KEY")

# Scrape a documentation page
docs = app.scrape(
    url="https://docs.example.com/api-reference",
    params={"formats": ["markdown"]}
)

print(docs["markdown"])

Node.js SDK

import Firecrawl from "@mendable/firecrawl-js";

const app = new Firecrawl({ apiKey: "fc-YOUR_API_KEY" });

// Search for relevant documentation
const results = await app.search("Next.js server actions guide", {
  limit: 5,
  scrapeOptions: {
    formats: ["markdown"],
  },
});

results.data.forEach((result) => {
  console.log(`${result.metadata.title}: ${result.metadata.url}`);
});

cURL

curl -X POST https://api.firecrawl.dev/v1/scrape \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://docs.example.com/quickstart",
    "formats": ["markdown"]
  }'

Caching and Performance

The MCP server automatically caches responses to reduce API calls and speed up repeated lookups:

Setting	Default	Notes
Cache duration	15 minutes	Configurable per installation
Cache scope	Per URL + parameters	Different formats cached separately
Rate limits	Your plan limits	MCP requests count against your API quota

Customer Stories

Botpress

Botpress uses Firecrawl to streamline knowledge base population for their chatbot platform. Instead of manually importing documentation, they scrape and ingest content automatically.

Answer HQ

Answer HQ leverages Firecrawl for importing website data to build intelligent support assistants. Their AI agents access current help documentation through Firecrawl's extraction pipeline.

Supported Platforms

Platform	MCP Support	Notes
Claude Desktop	Native	First-class MCP integration
Cursor	Native	Built-in MCP support
Claude Code	Native	Via .mcp.json configuration
VS Code	Community	Extensions available for MCP
Other IDEs	Expanding	Check your IDE for MCP plugin availability

Best Practices

1. Use caching wisely -- The 15-minute default cache is good for most documentation; increase it for stable reference docs
2. Scope your requests -- Scrape specific pages rather than crawling entire sites when you need targeted information
3. Combine with Search -- When you do not know the exact URL, use Search to find the right page first
4. Keep your API key secure -- Store it in environment variables, not in committed configuration files
5. Monitor usage -- MCP requests count against your API quota; keep an eye on consumption during heavy development sessions

Related Use Cases

• AI Platforms & LLM Training -- Build RAG systems and AI assistants with web data
• Deep Research -- Use Firecrawl for agentic research workflows
• Content Generation -- Generate documentation and content from web sources