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mcp-server-rag-web-browser

Organization
apify

A MCP Server for the RAG Web Browser Actor

Publisherapify
Repositorymcp-server-rag-web-browser
LanguageJavaScript
Forks
30
Stars
203
Available tools
0
Transport typestdio
Categories
LicenseApache-2.0
Links
  • Connect tools to AI workflows

    mcp-server-rag-web-browser exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    203 stars and 30 forks from the linked repository.

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.

This MCP server is deprecated in favor of mcp.apify.com

For the same functionality and much more, please use one of these alternatives:

🚀 Recommended: use mcp.apify.com

The easiest way to get the same web browsing capabilities is to use mcp.apify.com with default settings.

Benefits:

  • ✅ No local setup required
  • ✅ Always up-to-date
  • ✅ Access to 6,000+ Apify Actors (including RAG Web Browser)
  • ✅ OAuth support for easy connection
  • ✅ Dynamic tool discovery

Quick Setup:

  1. Go to https://mcp.apify.com
  2. Authorize the client (Claude, VS Code, etc.)
  3. Copy the generated MCP server configuration (or use OAuth flow if supported)
  4. Start using browsing & other tools immediately

🌐 Alternative: direct RAG Web Browser integration

You can also call the RAG Web Browser Actor directly via its HTTP/SSE interface.

Benefits:

  • ✅ Direct integration without mcp.apify.com
  • ✅ Real-time streaming via Server-Sent Events
  • ✅ Full control over the integration
  • ✅ No additional dependencies

Docs: Actor Documentation


🎯 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.

  • Web Search: Query Google Search, scrape top N URLs, and return cleaned content as Markdown
  • Single URL Fetching: Fetch a specific URL and return its content as Markdown
  • Local MCP Integration: Standard input/output (stdio) communication with AI clients

🧱 Components

Tools

  • name: search description: Query Google Search OR fetch a direct URL and return cleaned page contents. arguments:
    • query (string, required): Search keywords or a full URL. Advanced Google operators supported.
    • maxResults (number, optional, default: 1): Max organic results to fetch (ignored when query is a URL).
    • scrapingTool (string, optional, default: raw-http): One of browser-playwright | raw-http.
      • raw-http: Fast (no JS execution) – good for static pages.
      • browser-playwright: Handles JS-heavy sites – slower, more robust.
    • outputFormats (array of strings, optional, default: [markdown]): One or more of text, markdown, html.
    • requestTimeoutSecs (number, optional, default: 40, min 1 max 300): Total server-side AND client wait budget. A local abort is enforced.

🔄 Migration Guide

From Local MCP Server to mcp.apify.com

Before (Deprecated local server):

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

After (Recommended Apify server):

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

Or use the hosted endpoint: https://mcp.apify.com (when your client supports HTTP transport / remote MCP).

MCP clients

🛠️ Development

Prerequisites

  • Node.js (v18 or higher)
  • Apify API Token (APIFY_TOKEN)

Clone & install:

bash
git clone https://github.com/apify/mcp-server-rag-web-browser.git
cd mcp-server-rag-web-browser
npm install

Build

bash
npm install
npm run build

Debugging

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

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

bash
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.

📖 Learn more

This repository is maintained for archival purposes only. Please use the recommended alternatives above for active development.

Installation

TypingMind
Prerequisites:

Node.js 18+

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

Use mcp-server-rag-web-browser MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once mcp-server-rag-web-browser is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add mcp-server-rag-web-browser as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the mcp-server-rag-web-browser MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "mcp-server-rag-web-browser": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/mcp-server-rag-web-browser"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the mcp-server-rag-web-browser MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the mcp-server-rag-web-browser MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use mcp-server-rag-web-browser to help me with this task?
mcp-server-rag-web-browser
Sure. I read it.
Here is what I found using mcp-server-rag-web-browser.

Frequently asked questions

What is the mcp-server-rag-web-browser MCP server used for?

mcp-server-rag-web-browser is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use mcp-server-rag-web-browser MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use mcp-server-rag-web-browser with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use mcp-server-rag-web-browser MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With mcp-server-rag-web-browser connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect mcp-server-rag-web-browser MCP to TypingMind?

mcp-server-rag-web-browser runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does mcp-server-rag-web-browser MCP provide in TypingMind?

mcp-server-rag-web-browser exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use mcp-server-rag-web-browser MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If mcp-server-rag-web-browser requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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