How to Use the WebScrapingAPI MCP in LlamaIndex

Q: Can I index multiple search results using WebScrapingAPI and LlamaIndex?

Yes. You can run searchgoogleserp to get a list of search results, scrape each one individually, and then index the resulting text chunks into your knowledge base for later querying.

Q: What data types does WebScrapingAPI provide when used with LlamaIndex?

It provides multiple types: raw HTML (from scrapestatichtml), structured JSON (from scrapeecommerceproduct), and plain text derived from search engine results.

Q: If I use WebScrapingAPI to get product data, how does LlamaIndex handle it?

LlamaIndex indexes the structured JSON returned by scrapeecommerceproduct. You can then query based on fields like 'price' or 'title', making the data highly specific and useful.

Q: Does WebScrapingAPI provide good coverage for global indexing with LlamaIndex?

The searchyandexserp tool allows you to retrieve structured search results from Yandex, which you can then index into your knowledge base alongside Google or Bing data.

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Connect WebScrapingAPI MCP to LlamaIndex

Create your Vinkius account to connect WebScrapingAPI 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.

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Setup WebScrapingAPI with LlamaIndex

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Indexing Live Data for LlamaIndex

LlamaIndex doesn't just call tools; it ingests the results. When you run a scrape, that output—whether text or JSON from `scrape_and_auto_extract`—gets indexed into your vector store. This means you can query past API data months later and get answers grounded in actual web information, not hallucinations.

Retrieving E-commerce Product Knowledge

Build a knowledge base around product catalog research. Use `scrape_ecommerce_product` to gather structured JSON for multiple products from different stores. Indexing this allows users to ask, 'What were the average reviews for X brand across all indexed items?' It turns temporary API calls into permanent, queryable data assets.

Capturing Complex Web Pages with MCP Server

If a target page is rendered by JavaScript or requires mobile emulation, use `scrape_js_rendered` or `scrape_as_mobile`. You then index this rich, accurate content into LlamaIndex. This preserves the full context needed for deep semantic search. You get better retrieval because you're indexing the actual visual/functional state of the page.

Setup guide

Set up WebScrapingAPI 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 WebScrapingAPI 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 WebScrapingAPI tools.",
)
response = await agent.run("List recent WebScrapingAPI data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WebScrapingAPI. 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.

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Common questions about WebScrapingAPI MCP in LlamaIndex

The tool output is automatically passed to your vector store. If you use `scrape_static_html`, that raw HTML content becomes a chunk of searchable knowledge, allowing the agent to retrieve context from it later.

Yes. You can run `search_google_serp` to get a list of search results, scrape each one individually, and then index the resulting text chunks into your knowledge base for later querying.

It provides multiple types: raw HTML (from `scrape_static_html`), structured JSON (from `scrape_ecommerce_product`), and plain text derived from search engine results.

LlamaIndex indexes the structured JSON returned by `scrape_ecommerce_product`. You can then query based on fields like 'price' or 'title', making the data highly specific and useful.

The `search_yandex_serp` tool allows you to retrieve structured search results from Yandex, which you can then index into your knowledge base alongside Google or Bing data.

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