ZenRows MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ZenRows as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 ZenRows. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ZenRows?"
)
print(response)
asyncio.run(main())
* 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 ZenRows MCP Server
Connect your ZenRows account to any AI agent and harness the power of industrial-grade web scraping through natural conversation.
LlamaIndex agents combine ZenRows tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Universal Scraping — Retrieve raw HTML from any website while ZenRows automatically rotates proxies and handles CAPTCHAs
- JavaScript Rendering — Scrape dynamic SPAs and complex web apps by using a headless browser to capture the full rendered state
- Anti-Bot Bypass — Effortlessly bypass sophisticated protections like Cloudflare, DataDome, and PerimeterX with specialized bypass technology
- Markdown Conversion — Automatically convert web pages into clean Markdown, ideal for LLM ingestion and RAG applications
- Structured Data — Use auto-parse to extract JSON data from major e-commerce, search, and social platforms without manual selectors
- Visual Previews — Generate real-time screenshots of target pages to verify rendering or monitor visual changes
- Geographic Targeting — Execute scrapes using high-anonymity residential proxies from specific countries for localized content
The ZenRows MCP Server exposes 10 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 ZenRows to LlamaIndex via MCP
Follow these steps to integrate the ZenRows 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 10 tools from ZenRows
Why Use LlamaIndex with the ZenRows MCP Server
LlamaIndex provides unique advantages when paired with ZenRows through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ZenRows tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ZenRows tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ZenRows, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ZenRows tools were called, what data was returned, and how it influenced the final answer
ZenRows + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ZenRows MCP Server delivers measurable value.
Hybrid search: combine ZenRows real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ZenRows 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 ZenRows for fresh data
Analytical workflows: chain ZenRows queries with LlamaIndex's data connectors to build multi-source analytical reports
ZenRows MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ZenRows to LlamaIndex via MCP:
get_screenshot
Generates a URL that returns a screenshot of the target page
scrape_antibot
Enables js_render and antibot=true. Scrape with full anti-bot bypass for heavily protected sites
scrape_autoparse
Scrape with automatic structured data extraction
scrape_custom
g. wait, css_extractor, session_id). Execute a scrape using advanced custom parameters
scrape_geo
g. "us", "gb") for localized content. Scrape using a proxy from a specific country
scrape_html
ZenRows automatically rotates proxies and handles CAPTCHAs. Scrape raw HTML using ZenRows anti-bot proxy pool
scrape_js
Enables js_render=true. Slower and more expensive than static scraping. Scrape JS-rendered HTML using ZenRows headless browser
scrape_markdown
Automatically removes boilerplate like navigation and ads. Scrape and convert page content to clean Markdown
scrape_premium
Sets premium_proxy=true for higher anonymity. Scrape using ZenRows premium residential proxies
scrape_wait
g. "#results") to wait for before capturing the HTML. Scrape with JS render waiting for a specific CSS selector
Example Prompts for ZenRows in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ZenRows immediately.
"Scrape 'https://example.com' and return the content in Markdown."
"Bypass Cloudflare and scrape the rendered HTML of 'https://protected-site.com'."
"Get a screenshot of 'https://news-portal.com/breaking-news'."
Troubleshooting ZenRows MCP Server with LlamaIndex
Common issues when connecting ZenRows to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenRows + LlamaIndex FAQ
Common questions about integrating ZenRows MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ZenRows with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ZenRows to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.