If you have been experimenting with Claude or ChatGPT, you may know that we can’t interact with external tools, apps, and data sources to get what you need.
In short, you can’t ask ChatGPT or Claude to scrape Amazon and get the specific product details.
And that’s where Anthropic released the MCP protocol, which makes it very simple to interact with external tools and other systems directly from LLMs.
You can think of MCP as simply a way for AI models to connect with external tools, apps, and data sources to get what you need.
With that, you can fetch real data, run tools, read documents, and interact with other software.
To be more precise, you just need to simply ask the LLMs like:
- Get me product prices from this website
- Extract all startup names from this directory
- Scrape the emails from this page
And the AI does the rest, thanks to MCPs that let you scrape data.
Now, in the web scraping space, Octoparse and Apify provide their MCP servers so that you can scrape with a single prompt and then work with data right inside LLMs.
And in this post, I am going to break down:
- Why MCP matters and how it works
- What Octoparse MCP and Apify MCP actually do
- How difficult they are to set up in practice
- Which one is easier for non-coders
- When each MCP actually makes sense to use
With that said, let’s get into it.
Why MCP Matters & How It Works
Now you know that MCP acts as a USB between an AI model and external tools, and it just helps them connect.
But Nitin, why does it matter? Let me give you an example.
Suppose you ask an LLM: Find 50 SaaS companies in fintech and give me their emails.
Without MCP, as we know, we can’t fetch real-time data and even connect with external tools.
Because of this, every time someone wants an AI model to use a new data source, developers have to build a custom integration for it. This takes time and makes the system harder to scale.
The other option is to run a scraper, export CSV to LLM, and then upload data to the AI to get the answers.
That’s where Anthropic released MCP, which is simply one standard way for AI systems to connect with data sources. And so instead of building a different connection for every tool or database, developers can use one common protocol.
A later technical write‑up on MCP highlights that its goal is to give AI agents a single, universal interface for executing tools and accessing external data, instead of one‑off integrations for every new system.
The best part? The LLM can directly call a scraping tool through the MCP process, get the data, process it, and return the result as you want.
Yes, it’s that easy.
But Nitin, how does MCP work? Well, it has mainly three components i.e., MCP Host, MCP Client, and MCP Server.
Let me give you an example to help you understand it completely:
- Here, MCP Host is mainly the application that runs the AI model and manages the conversation. You can think of MCP hosts as Claude Desktop, AI IDEs, or agent frameworks.
- The MCP client is a component inside the host, and its job is only communication. It connects to MCP servers, sends tool requests, and receives results.
- Lastly, the MCP server is the external tool provider that exposes capabilities the AI can use. Like Octoparse provides the Octoparse MCP server or Apify provides the Apify MCP Server.
Now, let’s specifically talk about what Apify & Octoparse MCP is, and how to use it while scraping.
Apify MCP Review
What Is Apify MCP and How to Get Started?
Apify is another web scraping platform that offers an MCP server. It uses a developer-oriented model based on individual scraping programs called ‘Actors.’
But Nitin, what does Apify MCP do? Well, it lets AI tools like ChatGPT, Claude, or Cursor interact directly with Apify’s web scraping platform. This means you can trigger Apify Actors, extract structured data, and run scraping tasks simply by writing prompts.
Here’s how to connect the Apify MCP Server with Claude:
Now, Apify is a developer-focused scraping platform that has existed for years.
Instead of building one scraper for each website manually, Apify provides something called Actors.
Actors are basically small scraping programs designed to extract specific data from websites, and they have 18,875 actors when I’m writing this post.
To be more precise, there are actors like:
- Website Content Crawler
- Instagram Scraper
- LinkedIn Post Scraper
- And more
That can scrape content based on the name suggested.
And once the MCP integration is enabled, Claude or another AI can run these actors directly.
Then the workflow looks like this:
You ask the AI something like: Get the top restaurants in London from Google Maps.
Claude then calls the Google Maps scraper actor inside Apify, runs it, and returns the results.
Where Apify MCP Becomes Complicated for Non-Coders
The first challenge appears during setup.
To connect Apify to Claude through MCP, you usually need to:
- create an Apify account
- generate an API token
- configure the MCP server
- find the actors and add them if you want to scrape something specific
And so, if you are comfortable reading documentation and dealing with configuration files, this is manageable.
But if you are a non-technical user, it can feel confusing very quickly, and it will become hard to even set up.
Then comes the second challenge: choosing the right actor, and adding them inside the config file.
That’s not all. Apify has thousands of them, and each has its own documentation about how to use it.
That sounds great until you realize you now have to figure out:
- which actor actually works
- which one is maintained
- what inputs it expects
So even though the AI runs the actor for you, the workflow still assumes some technical understanding.
This is why Apify MCP feels powerful but slightly heavy for beginners.
Octoparse MCP Review
What Is Octoparse MCP, and How to Get Started?
You know, Octoparse takes a completely different approach to help you scrape.
It mainly focuses on visual scraping workflows using their point-and-click interface.
If you have ever used a no-code scraper or a no-code website builder, the process looks familiar.
You open a webpage and simply:
- click the elements you want to extract
- define fields like name, price, or email
- let the scraper navigate the pages automatically
The best part? Octoparse already has millions of users doing this, and they even provide hundreds of prebuilt templates, solve anti-bot techniques easily, and even allow scheduling scraping tasks.
What the MCP integration does is connect those scraping tasks directly to AI assistants. So Claude or ChatGPT can trigger your Octoparse workflows automatically.
In simple terms, Octoparse MCP allows you to connect with ChatGPT, Claude, Cursor, or even the terminal, and you can perform complex web scraping by writing simple prompts.
Here’s how to connect Octoparse MCP with Claude and with ChatGPT.
And after connecting, the AI can search pre-built web scraping templates, scrape data, create custom web scraping tasks, initiate cloud extraction, and more.
Why Octoparse MCP Feels Much Easier for Non-Coders
Now, as you know the way Apify and Octoparse work, so the usual difference is only while doing the setup and in the scraping process.
First, connecting the Octoparse MCP takes about 3 minutes. Let’s say connecting it with Claude. You just add a custom connector in Claude Desktop, paste the remote MCP server URL (https://mcp.octoparse.com), and authorize with your Octoparse account. No API tokens to generate, no config files to edit, no actors to add manually.
And as we know, both provide web scraping templates to make the process simpler and save you time.
But with Apify, you usually need to find the actor, add the actor, understand the way to use it, and then scrape the output.
And with Octoparse, you simply need to ask for the template you need, create a task to scrape, and export the output instantly.
For example, imagine you want to scrape startup listings from a directory.
Inside Octoparse you would:
- connect the Octoparse MCP visually
- ask the LLM to find a template that can scrape
- create a task to scrape
- and then export it
Yes, it’s that easy, and that makes the scraping process more intuitive for a non-coder.
Also, one common issue with Apify is that it often throws errors that take time to fix. Octoparse MCP usually does not have these kinds of problems.
Independent review platforms also highlight that Octoparse makes web scraping accessible even for users without technical skills, praising its user‑friendly interface, no‑code functionality, and pre‑built templates that reduce setup time.
A Real Example of Apify and Octoparse MCP Workflows
You see, we have learned everything about what MCP is, why it matters, and then learned about Apify MCP and Octoparse MCP.
But we haven’t seen a real, practical workflow, so let’s talk about that.
Let’s say you want to build a list of SaaS companies from a directory website.
Here is how the process differs.
Using Octoparse MCP
You need first download Octoparse and create an account. Then you can:
- configure the MCP visually
- ask the LLMs to find an Octoparse template that fits your needs (multiple templates combined are supported)
- ask the LLMs to create a task using the appropriate templates
- start the task
- export all data as CSV
- ask the LLMs to analyze your data
Based on the above steps, here’s the output:
Using Apify MCP
You first need to:
- configure the MCP integration through a complex setup process
- find an actor capable of scraping the site,
- add the specific actor to the config file
- let Claude or ChatGPT run the actor
Based on the above steps, here’s the output:
Which MCP Should You Actually Use?
Now, I can’t say that you should simply use Octoparse becauseApify can also scale.
And it has a huge ecosystem of actors which can save a lot of time, so it depends on what you do and the features you need.
So, here’s a table comparing its features and more:
And now, to give you some more ideas, use Octoparse MCP if:
- you are a non-technical user
- you want to use pre-built templates for all popular scraping scenarios
- you enjoy cloud-based, 24/7, high-speed, and large-scale data harvesting
- you want to build custom scrapers without coding
- you mainly scrape directories, listings, or websites
- you prefer uninterrupted data collection with built-in IP rotation and CAPTCHA bypass.
Use Apify MCP if:
- you are comfortable with coding
- you want access to thousands of existing scrapers
- you need large scale automation
- you are building AI agents or complex workflows
In other words, Octoparse focuses on simplicity, while Apify focuses on power and flexibility.
FAQs About Octoparse MCP vs Apify MCP
1. Do I need to know coding to use MCP for web scraping?
Not necessarily.
And MCP itself is just the protocol that makes the process easier and allows AI models to connect with external tools like Octoparse or Apify. The technical complexity mainly depends on the tool you connect to the MCP server.
For example, if you use Apify MCP, you need to add a couple of lines of code, API tokens, and actor inputs. That means you should at least understand basic developer concepts.
2. Can Claude or ChatGPT scrape any website using MCP?
The answer is simply “no”.
MCP only allows the AI model to call external tools, but the actual scraping still depends on the tool you connect.
For example: If you connect Octoparse MCP, Claude can trigger your Octoparse scraping workflows.
And Octoparse can easily solve CAPTCHAs, auto-detect page elements, provide tons of web scraping templates, and so on.
But if the tool you connect can’t scrape a specific website because of login walls, anti-bot protection, or dynamic loading, MCP cannot magically bypass that.
So the AI is only as capable as the scraping tool it is connected to.
3. Can I use MCP with tools other than Octoparse and Apify?
Yes, MCP is not limited to web scraping tools.
The protocol is designed to connect AI models with any external tool or data source that exposes an MCP server. This could include databases, APIs, file systems, automation tools, and many other services.
Octoparse and Apify are just two popular examples in the scraping space.
And as MCP adoption grows, you will likely see many more tools exposing MCP servers so AI assistants can interact with them directly.
4. Can MCP automate the entire data collection process with AI?
Well, it depends on the scraping tool you use.
For instance, we know that using Octoparse MCP we can easily scrape websites, and then the LLM can analyze the results as well.
And further, Octoparse supports automation since it has all the capabilities like handling login requirements, anti-bot protection, and dynamic content restrictions.
But other MCPs cannot bypass those limitations.
In short, MCP makes the automation process simpler, but what is technically possible still depends on the scraping tool behind it.
