MCP Server RAG Web Browser
by
Provides a fast, stdio‑based server that enables AI agents to perform web searches and fetch page content via the RAG Web Browser Actor.
MCP Server RAG Web Browser Overview
What is MCP Server RAG Web Browser about?
Enables AI agents and large language models to interact with the web in real time. The server runs locally, communicates over standard input/output, and forwards search or fetch requests to Apify's RAG Web Browser Actor, returning cleaned content as Markdown (or other formats).
How to use MCP Server RAG Web Browser?
- Install dependencies
git clone https://github.com/apify/mcp-server-rag-web-browser.git cd mcp-server-rag-web-browser npm install npm run build - Configure your MCP client (e.g., Claude Desktop) by adding the following entry to
claude_desktop_config.json:"mcpServers": { "rag-web-browser": { "command": "npx", "args": ["-y", "@apify/mcp-server-rag-web-browser"], "env": { "APIFY_TOKEN": "your-apify-api-token" } } } - Restart the client and look for the 🔌 icon to confirm the connection.
- Issue commands such as a web search or fetch a single URL through the client’s natural‑language interface.
Key features of MCP Server RAG Web Browser
- StdIO communication – simple integration with any MCP‑compatible AI client.
searchtool – query Google, scrape top N results, and return cleaned content.- Flexible output – choose between text, markdown, or HTML formats.
- Configurable scraping – use either Playwright‑based browser or raw HTTP.
- Timeout control – set request timeout per call.
- Local execution – no need for remote servers, ensuring fast response times.
Use cases of MCP Server RAG Web Browser
- Enabling ChatGPT‑like agents to browse the web for up‑to‑date information.
- Research assistants that automatically gather and summarize recent articles.
- Customer‑support bots that pull product documentation from live websites.
- Content creation tools that fetch reference material on demand.
FAQ from the MCP Server RAG Web Browser
Q: Which environments are supported? A: macOS, Windows, and any platform with Node.js v18+.
Q: Do I need an Apify account?
A: Yes, an Apify API token (APIFY_TOKEN) is required to run the RAG Web Browser Actor.
Q: Can I change the scraping method?
A: Yes, set scrapingTool to browser-playwright or raw-http when calling the search tool.
Q: How many results can I request?
A: Use the maxResults parameter; the default is 1.
Q: How do I debug the server? A: Use the Model Context Protocol Inspector:
export APIFY_TOKEN=your-token
npx @modelcontextprotocol/inspector npx -y @apify/mcp-server-rag-web-browser
MCP Server RAG Web Browser's README
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 URLmaxResults(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" } } } - On macOS:
-
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.
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MCP Server RAG Web Browser's Information
Configuration
{
"mcpServers": {
"rag-web-browser": {
"command": "npx",
"args": [
"-y",
"@apify/mcp-server-rag-web-browser"
],
"env": {
"APIFY_TOKEN": "<YOUR_API_TOKEN>"
}
}
}
}Configure Clients
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