How to Use the Crawlbase MCP in LlamaIndex

Q: How do I connect Crawlbase to LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient pointing to your Vinkius URL. Wrap it with McpToolSpec and pass the tools to your FunctionAgent.

Feed live web data into your LlamaIndex knowledge base using the Crawlbase MCP Server to scrape JS-rendered pages.

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Connect Crawlbase MCP to LlamaIndex

Create your Vinkius account to connect Crawlbase 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 Crawlbase with LlamaIndex

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Claude

Perplexity

Populating vector stores with HTML extraction

RAG applications die without fresh data. Hooking up this MCP Server lets your indexing engine pull live content from any website. You call `scrape_html` and instantly get clean text to chunk and embed. Modern sites hide everything behind JavaScript. That means standard scrapers return empty div tags. Running `scrape_js_rendered` forces the headless engine to wait for the payload, giving your index actual content to search against.

Grounding LlamaIndex queries in social data

Answering questions about real-time events requires scraping social platforms. Your agent can trigger `scrape_twitter` or `scrape_facebook` to pull active social pages. Those extracted posts become searchable nodes in your index. Professional profiles require strict structural matching. Firing `scrape_linkedin` verifies blueprint constraints and returns clean professional histories. Your query engine reads actual API data instead of hallucinating job titles.

E-commerce and search ingestion

Tracking market positioning means indexing search results. `scrape_google_serp` identifies precise active arrays from search engines and feeds them into your document store. You can query past ranking sessions with complete accuracy. Product data changes hourly. Hitting `scrape_amazon` inspects deep internal arrays to pull pricing and reviews. If you need visual records, `get_screenshot_link` drops a snapshot URL right into your metadata.

Setup guide

Set up Crawlbase 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 Crawlbase 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 Crawlbase tools.",
)
response = await agent.run("List recent Crawlbase 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 Crawlbase. 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 Crawlbase MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` pointing to your Vinkius URL. Wrap it with `McpToolSpec` and pass the tools to your `FunctionAgent`.

The agent can fetch live product pages using the Amazon-specific tool. It indexes the pricing and description data into your vector store. You then query that fresh data directly.

Traditional loaders fail on SPAs. This server provides a dedicated JavaScript rendering tool that executes the page scripts before returning the DOM. Your index gets the actual rendered text.

The tools automatically route through a massive proxy network. They bypass CAPTCHAs and IP bans without any configuration on your end. Your indexing pipeline stays green.

Search queries and extracted DOM text exist only during the active request. We run this MCP Server in an isolated, ephemeral container. Nothing persists after your LlamaIndex workflow completes.

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