ScraperAPI 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 ScraperAPI as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 ScraperAPI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ScraperAPI?"
)
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 ScraperAPI MCP Server
Connect your ScraperAPI account to any AI agent to bypass IP bans, CAPTCHAs, and complex anti-bot systems. Allow your agent to scrape the web dynamically using a pool of millions of proxies.
LlamaIndex agents combine ScraperAPI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Render JavaScript — Command your AI to fetch data from SPAs (Single Page Applications) like React or Vue sites flawlessly
- Structured E-commerce & SEO — Extract parsed Amazon product pages via ASIN or pull Google SERP attributes in structured JSON formats directly to your chat
- Premium Unblocking — Access high-quality residential proxies automatically when dealing with ultra-secure or aggressive Cloudflare-protected targets
- Asynchronous & Visual Scraping — Spawn background scraping jobs for slow-loading pages or ask the AI to generate full-page screenshot URLs upon request
The ScraperAPI 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 ScraperAPI to LlamaIndex via MCP
Follow these steps to integrate the ScraperAPI 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 ScraperAPI
Why Use LlamaIndex with the ScraperAPI MCP Server
LlamaIndex provides unique advantages when paired with ScraperAPI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ScraperAPI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ScraperAPI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ScraperAPI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ScraperAPI tools were called, what data was returned, and how it influenced the final answer
ScraperAPI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ScraperAPI MCP Server delivers measurable value.
Hybrid search: combine ScraperAPI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ScraperAPI 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 ScraperAPI for fresh data
Analytical workflows: chain ScraperAPI queries with LlamaIndex's data connectors to build multi-source analytical reports
ScraperAPI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ScraperAPI to LlamaIndex via MCP:
create_async_job
Returns a job ID. Creates an asynchronous scraping job
custom_scrape
Performs a scrape with custom ScraperAPI parameters
get_account_stats
Retrieves API usage statistics
get_async_job
Retrieves the status and result of an async job
get_screenshot_link
Generates a URL to capture a full-page screenshot
scrape_amazon
Retrieves structured Amazon product details
scrape_google_serp
Retrieves structured Google Search results
scrape_html
Automatically rotates proxies. Scrapes standard HTML from a URL
scrape_js_rendered
Scrapes a URL with JavaScript rendering enabled
scrape_premium
Scrapes a URL using high-quality residential proxies
Example Prompts for ScraperAPI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ScraperAPI immediately.
"Scrape an Amazon product page for this ASIN: B08J5F3G18 and list its price."
"Run a Google SERP check for the keyword 'best LLM orchestration frameworks'."
"Take a screenshot of https://netflix.com homepage so I can check its layout."
Troubleshooting ScraperAPI MCP Server with LlamaIndex
Common issues when connecting ScraperAPI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpScraperAPI + LlamaIndex FAQ
Common questions about integrating ScraperAPI 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 ScraperAPI 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 ScraperAPI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.