How to Use the ZenRows MCP in LlamaIndex

Q: How does LlamaIndex use ZenRows for structured data?

You run scrapeautoparse to extract the necessary fields, and then pass those clean records into your index. This ensures that when a user queries the knowledge base, they get precise values, not just paragraphs of text.

Q: Can LlamaIndex use ZenRows for large-scale indexing?

Yes. scrapehtml handles raw scraping while automatically rotating proxies and managing CAPTCHAs. This robustness means your index build process won't halt due to network blocks.

Q: Does LlamaIndex support ZenRows for JS-rendered content?

The scrapejs tool enables JavaScript rendering, letting you capture dynamic page content. This is crucial because much of the data needed for your index only loads after client-side scripts run.

Q: Can I use ZenRows with LlamaIndex to compare regions?

You can build multiple indexes using scrapegeo for different countries. When querying, LlamaIndex combines these sources so you get a comprehensive answer grounded in specific regional data.

Build knowledge-augmented AI with LlamaIndex and ZenRows MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect ZenRows MCP to LlamaIndex

Create your Vinkius account to connect ZenRows to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup ZenRows with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Create reliable data chunks with the ZenRows MCP Server.

LlamaIndex indexes results, so you need clean source material. Use `scrape_markdown` to extract content while automatically removing site clutter like ads or footers. This gives your vector store pure text. The structured output means your RAG application searches against high-quality document chunks, minimizing retrieval errors.

Index difficult web data using LlamaIndex.

For heavily protected sites, start with `scrape_antibot`. The resulting raw data is perfect for indexing because it captures the full context needed to answer complex queries. Your knowledge base can then retrieve specific details that would otherwise be invisible to a basic scraper.

Source diverse information via LlamaIndex.

Need data from different geographic markets? Run `scrape_geo` with country parameters. This allows you to build separate, specialized indexes for localized content. LlamaIndex can then combine these regional knowledge sources into one unified queryable index.

Setup guide

Set up ZenRows MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ZenRows MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ZenRows tools.",
)
response = await agent.run("List recent ZenRows data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ZenRows. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect ZenRows now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about ZenRows MCP in LlamaIndex

You run `scrape_autoparse` to extract the necessary fields, and then pass those clean records into your index. This ensures that when a user queries the knowledge base, they get precise values, not just paragraphs of text.

Yes. `scrape_html` handles raw scraping while automatically rotating proxies and managing CAPTCHAs. This robustness means your index build process won't halt due to network blocks.

The `scrape_js` tool enables JavaScript rendering, letting you capture dynamic page content. This is crucial because much of the data needed for your index only loads after client-side scripts run.

You can build multiple indexes using `scrape_geo` for different countries. When querying, LlamaIndex combines these sources so you get a comprehensive answer grounded in specific regional data.

The server primarily touches raw HTML, structured text (JSON/schema), and scraped document content. This is the data that gets indexed into your knowledge base.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript