Can it read documentation sites that are split into multiple pages?

Yes! You can use the `crawl` tool. For example: 'Crawl the getting started guide at https://example.com/docs'. The agent will fetch the starting page and automatically follow inner links to gather up to 10 pages of context.

How does it handle ads and cluttered websites?

The `read` tool uses the same underlying technology as Firefox's 'Reader View' (@mozilla/readability). It intelligently strips out standard website boilerplate—like navbars, sidebars, footers, and ads—leaving only the title and the clean main article text converted to Markdown.

Is there a limit on how many URLs I can batch process?

Yes, to ensure conversational AI latency remains reasonable, the `batch_read` tool accepts a maximum of 10 URLs in a single request. All 10 URLs are fetched simultaneously in parallel for maximum speed.

Web Scraper MCP Server for LlamaIndex 5 tools — connect in under 2 minutes

Name: Web Scraper
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Web Scraper as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Web Scraper. "
            "You have 5 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Web Scraper?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Web Scraper MCP Server

Connect the Web Scraper utility to any AI agent to give it direct access to the public internet. Instead of letting the AI hallucinate facts, allow it to read real-time articles, parse documentation, and fetch clean text from any URL you provide.

LlamaIndex agents combine Web Scraper tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Reader View — Convert any cluttered webpage into pristine, readable Markdown by stripping out ads, navbars, and boilerplate using Mozilla Readability logic
Site Crawling — Instruct the AI to crawl a starting URL (like a documentation hub or wiki) up to 10 pages deep automatically
Batch Processing — Fetch up to 10 different URLs in parallel to compare articles or summarize multiple sources at once
Metadata Extraction — Quickly pull SEO titles, descriptions, OG tags, canonical links, and all outbound hyperlinks without downloading the entire page body

The Web Scraper MCP Server exposes 5 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Web Scraper to LlamaIndex via MCP

Follow these steps to integrate the Web Scraper MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 5 tools from Web Scraper

Why Use LlamaIndex with the Web Scraper MCP Server

LlamaIndex provides unique advantages when paired with Web Scraper through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Web Scraper tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Web Scraper tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Web Scraper, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Web Scraper tools were called, what data was returned, and how it influenced the final answer

Web Scraper + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Web Scraper MCP Server delivers measurable value.

Hybrid search: combine Web Scraper real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Web Scraper to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Web Scraper for fresh data

Analytical workflows: chain Web Scraper queries with LlamaIndex's data connectors to build multi-source analytical reports

Web Scraper MCP Tools for LlamaIndex (5)

These 5 tools become available when you connect Web Scraper to LlamaIndex via MCP:

batch_read

All URLs are fetched in parallel. Maximum 10 URLs per batch. Fetch multiple web pages in parallel

crawl

Maximum 10 pages to keep response size manageable. Crawl a website starting from a URL

extract

Returns: title, description, OG tags, lang, author, robots, canonical, link count. For the full page content, use the read tool instead. Extract structured metadata from a web page: title, description, OG tags, and more

list_links

Internal links share the same hostname as the source page. Extract all hyperlinks from a web page

read

Uses @mozilla/readability (Firefox Reader View) to extract the main article content, then converts to Markdown. Works best for articles, docs, blogs, and Wikipedia. Fetch any public web page and return its full content as clean Markdown

Example Prompts for Web Scraper in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Web Scraper immediately.

"Read https://en.wikipedia.org/wiki/Artificial_intelligence and summarize its history."

"Extract the links from https://news.ycombinator.com/"

"Compare these two links: url1.com and url2.com"

Troubleshooting Web Scraper MCP Server with LlamaIndex

Common issues when connecting Web Scraper to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Web Scraper + LlamaIndex FAQ

Common questions about integrating Web Scraper MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Web Scraper tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.