Model Context Protocol (mcp) Server For The Rag Web Bro...

Overview

What is MCP Server for RAG Web Browser?

The MCP Server for RAG Web Browser is a powerful tool designed to enhance the functionality of web scraping and automation tasks. It serves as a backend server that facilitates the operation of the RAG (Retrieval-Augmented Generation) Web Browser Actor, enabling users to efficiently gather and process web data. This server is particularly useful for developers and data scientists who require a robust solution for web data extraction and manipulation.

Features of MCP Server for RAG Web Browser

Seamless Integration: The MCP Server integrates effortlessly with the RAG Web Browser, allowing for smooth operation and data retrieval.
Scalability: Designed to handle multiple requests simultaneously, the server can scale according to user needs, making it suitable for both small and large projects.
User-Friendly Interface: The server provides an intuitive interface that simplifies the configuration and management of web scraping tasks.
Robust Performance: With optimized algorithms, the MCP Server ensures fast and reliable data processing, minimizing downtime and maximizing efficiency.
Support for Various Data Formats: The server can handle different data formats, making it versatile for various applications in data analysis and reporting.

How to Use MCP Server for RAG Web Browser

Installation: Begin by installing the MCP Server on your local machine or server. Follow the installation instructions provided in the documentation.
Configuration: Configure the server settings to match your project requirements. This includes setting up API keys, data formats, and other preferences.
Integration: Connect the MCP Server with the RAG Web Browser Actor. This step is crucial for enabling data retrieval and processing capabilities.
Execution: Start the server and execute your web scraping tasks. Monitor the performance and adjust settings as necessary to optimize results.
Data Management: Once data is collected, use the server's tools to manage, analyze, and export the data in your desired format.

Frequently Asked Questions

Q: What is the primary use of the MCP Server for RAG Web Browser?

A: The MCP Server is primarily used for web scraping and automation tasks, allowing users to efficiently gather and process data from various websites.

Q: Is the MCP Server suitable for large-scale projects?

A: Yes, the MCP Server is designed to be scalable, making it suitable for both small and large-scale projects.

Q: Can I customize the server settings?

A: Absolutely! The MCP Server allows for extensive customization of settings to meet specific project needs.

Q: What types of data formats does the server support?

A: The MCP Server supports various data formats, including JSON, CSV, and XML, making it versatile for different applications.

Q: Where can I find more information about the MCP Server?

A: For more detailed information, you can visit the official Apify documentation or the GitHub repository for the MCP Server.

Details

Model Context Protocol (MCP) Server for the RAG Web Browser Actor 🌐

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

🎯 What does this MCP server do?

This server is specifically designed to provide fast responses to AI agents and LLMs, allowing them to interact with the web and extract information from web pages. It runs locally and communicates with the RAG Web Browser Actor in Standby mode, sending search queries and receiving extracted web content in response.

The RAG Web Browser Actor allows an AI assistant to:

Perform web search, scrape the top N URLs from the results, and return their cleaned content as Markdown
Fetch a single URL and return its content as Markdown

🧱 Components

Tools

search: Query Google Search, scrape the top N URLs from the results, and returns their cleaned content as Markdown. Arguments:
- query (string, required): Search term or URL
- maxResults (number, optional): Maximum number of search results to scrape (default: 1)
- scrapingTool (string, optional): Select a scraping tool for extracting web pages. Options: 'browser-playwright' or 'raw-http' (default: 'raw-http')
- outputFormats (array, optional): Select one or more formats for the output. Options: 'text', 'markdown', 'html' (default: ['markdown'])
- requestTimeoutSecs (number, optional): Maximum time in seconds for the request (default: 40)

🔄 What is the Model Context Protocol?

The Model Context Protocol (MCP) is a framework that enables AI applications, such as Claude Desktop, to connect seamlessly with external tools and data sources. For more details, visit the Model Context Protocol website or read the blog post What is MCP and why does it matter?.

🤖 How does the MCP Server integrate with AI Agents?

The MCP Server empowers AI Agents to perform web searches and browsing using the RAG Web Browser Actor. For a comprehensive understanding of AI Agents, check out our blog post: What are AI Agents? and explore Apify's Agents.

Interested in building and monetizing your own AI agent on Apify? Check out our step-by-step guide for creating, publishing, and monetizing AI agents on the Apify platform.

🔌 Related MCP servers and clients by Apify

This server operates over standard input/output (stdio), providing a straightforward connection to AI Agents. Apify offers several other MCP-related tools:

Server Options

🖥️ This MCP Server – A local stdio-based server for direct integration with Claude Desktop
🌐 RAG Web Browser Actor via SSE – Access the RAG Web Browser directly via Server-Sent Events without running a local server
🇦 MCP Server Actor – MCP Server that provides AI agents with access to over 4,000 specialized Apify Actors

Client Options

💬 Tester MCP Client – A user-friendly UI for interacting with any SSE-based MCP server

🛠️ Configuration

Prerequisites

MacOS or Windows
The latest version of Claude Desktop must be installed (or another MCP client)
Node.js (v18 or higher)
Apify API Token (APIFY_TOKEN)

Install

Follow the steps below to set up and run the server on your local machine: First, clone the repository using the following command:

git clone git@github.com:apify/mcp-server-rag-web-browser.git

Navigate to the project directory and install the required dependencies:

cd mcp-server-rag-web-browser
npm install

Before running the server, you need to build the project:

npm run build

Claude Desktop

Configure Claude Desktop to recognize the MCP server.

Open your Claude Desktop configuration and edit the following file:

On macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

"mcpServers": {
  "rag-web-browser": {
    "command": "npx",
    "args": [
      "@apify/mcp-server-rag-web-browser"
    ],
    "env": {
       "APIFY_TOKEN": "your-apify-api-token"
    }
  }
}

Restart Claude Desktop
- Fully quit Claude Desktop (ensure it's not just minimized or closed).
- Restart Claude Desktop.
- Look for the 🔌 icon to confirm that the server is connected.

Examples

You can ask Claude to perform web searches, such as:

What is an MCP server and how can it be used?
What is an LLM, and what are the recent news updates?
Find and analyze recent research papers about LLMs.

Debug the server using the MCP Inspector

export APIFY_TOKEN=your-apify-api-token
npx @modelcontextprotocol/inspector npx -y @apify/mcp-server-rag-web-browser

👷🏼 Development

Local client (stdio)

To test the server locally, you can use example_client_stdio.ts:

export APIFY_TOKEN=your-apify-api-token
node dist/example_client_stdio.js

The script will start the MCP server, fetch available tools, and then call the search tool with a query.

Direct API Call

To test calling the RAG Web Browser Actor directly:

export APIFY_TOKEN=your-apify-api-token
node dist/example_call_web_browser.js

Debugging

Since MCP servers operate over standard input/output (stdio), debugging can be challenging. For the best debugging experience, use the MCP Inspector.

Build the mcp-server-rag-web-browser package:

npm run build

You can launch the MCP Inspector via npm with this command:

export APIFY_TOKEN=your-apify-api-token
npx @modelcontextprotocol/inspector node dist/index.js

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

{ "mcpServers": { "mcp-server-rag-web-browser": { "command": "docker", "args": [ "run", "-i", "--rm", "ghcr.io/metorial/mcp-container--apify--mcp-server-rag-web-browser--mcp-server-rag-web-browser", "npm run start" ], "env": { "APIFY_TOKEN": "apify-token" } } } }

Model Context Protocol (mcp) Server For The Rag Web Browser Actor 🌐