Crawlbase MCP: Connect Your AI Agents to Real-Time Web Data

If you've ever felt frustrated by AI models that rely on outdated information, you're not alone. Traditional LLMs work with static training data that's often months or years behind. But what if your AI could pull fresh content directly from the web, right when you need it?

That's exactly what Crawlbase MCP does. It's a Model Context Protocol server that connects AI agents to live web content, giving them access to real-time data without the headaches of web scraping.

What Makes Crawlbase MCP Different

Think of Crawlbase MCP as a bridge between your AI tools and the constantly updating internet. Instead of guessing or working with stale information, your AI agents can now fetch current data on demand.

The system runs on Crawlbase's infrastructure, which already powers web scraping for over 70,000 developers worldwide. It handles all the technical complexity that usually makes web scraping a nightmare:

• JavaScript rendering for sites that load content dynamically

• Automatic proxy rotation to avoid getting blocked

• Multiple output formats including HTML, Markdown, and screenshots

What this means in practice: you ask your AI to check something on a website, and it actually goes there and gets the latest information. No workarounds, no manual copying and pasting.

How the Setup Actually Works

Getting started is simpler than you might expect. The whole process breaks down into three steps.

First, grab your API tokens. Head over to Crawlbase and sign up for a free account. You'll get two types of tokens: Normal tokens for standard HTML pages, and JavaScript tokens for modern web apps that need rendering.

Next, configure your MCP client. Whether you're using Claude, Cursor IDE, or Windsurf IDE, you'll add Crawlbase MCP to your server settings. This tells your AI tool where to send requests when it needs web data.

If you're working in a team environment or building custom integrations, there's also an HTTP mode that lets multiple users share a single server. It includes per-request authentication, so different team members can use their own tokens without conflicts.

Finally, start making requests. Once everything's connected, your AI can use simple commands to fetch web content in whatever format you need.

👉 Get instant access to real-time web scraping with Crawlbase's proven infrastructure

The Three Core Commands You'll Use

Crawlbase MCP gives you three main tools, each designed for a different type of web content extraction:

crawl returns raw HTML from any webpage. This is perfect when you need the complete source code or want to preserve the exact structure of a page.

crawl_markdown converts web content into clean, readable Markdown format. It strips away all the noise—ads, navigation menus, scripts—and leaves you with just the actual content. Great for articles, documentation, or any text-heavy page.

crawl_screenshot captures visual snapshots of websites. When you need to see how something actually looks or document the current state of a page, screenshots do the job.

You interact with these through natural language. Just tell your AI what you want:

"Crawl Hacker News and return the top stories in markdown."

"Take a screenshot of the TechCrunch homepage."

"Fetch the Tesla investor relations page as HTML."

Your AI understands these requests, routes them through Crawlbase MCP, and brings back the data you asked for.

Why This Matters for AI Workflows

The real power shows up when you start building more complex workflows. Imagine an AI assistant that monitors competitor pricing by checking their websites daily. Or one that summarizes breaking news from multiple sources in real time. Or tracks product launches across industry sites.

Before Crawlbase MCP, you'd need to build and maintain your own scraping infrastructure. That means dealing with proxies, handling JavaScript rendering, working around bot detection systems, and keeping everything running smoothly. It's a full-time job.

Now your AI just makes a request, and Crawlbase handles all of that in the background. The technical complexity disappears, and you can focus on what your AI actually does with the data.

This works especially well for research assistants that need current information, monitoring tools that track changes across websites, content aggregators that pull from multiple sources, and data collection pipelines that feed into other systems.

👉 Start building AI agents with live web access using Crawlbase's developer-friendly tools

Multi-User and Custom Integration Options

For teams or custom applications, the HTTP transport mode opens up additional possibilities. You can run a single Crawlbase MCP server that multiple users access simultaneously, each with their own authentication tokens.

This approach works through standard HTTP endpoints. The server exposes a POST endpoint for MCP requests and a GET endpoint for health checks. Users pass their Crawlbase tokens via request headers, which override any default environment variables.

The benefit here is centralization. Instead of every developer or AI agent maintaining its own connection, they all route through one shared server. This simplifies deployment, makes monitoring easier, and gives you a single point for configuration changes.

Getting Started Today

Web scraping doesn't have to be complicated, and your AI agents shouldn't be limited to outdated training data. Crawlbase MCP gives you a straightforward way to connect AI tools to real-time web content, backed by infrastructure that's already proven at scale.

The free tier gives you enough tokens to test it out and build prototypes. As your needs grow, the system scales with you—same simple commands, same reliable infrastructure, just more capacity.

Whether you're building research tools, monitoring systems, or data collection pipelines, having access to current web data changes what's possible. Your AI can finally work with the real, live internet instead of memories of it.