How to Use the Browserhub MCP in LlamaIndex

Q: Can I list available Browserhub scrapers in LlamaIndex?

Absolutely. Use listscrapers to retrieve a list of all your configured jobs. Your agent can then select the right scraper to populate your index.

Index live web content into your LlamaIndex knowledge base for semantic search.

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Connect Browserhub MCP to LlamaIndex

Create your Vinkius account to connect Browserhub 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 Browserhub with LlamaIndex

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Convert Browserhub results for LlamaIndex

Use `run_scraper` to pull raw web data and push it directly into your vector index. LlamaIndex then makes this information searchable for your RAG applications. This turns transient web pages into persistent knowledge. Your agents can query past scraping results as if they were local documents.

Dynamic scraping via LlamaIndex agents

Give your agents the ability to run `direct_scrape` whenever they encounter an unknown URL during a query. It lets your system fetch fresh context on the fly. Your index stays relevant because the agent decides what to scrape based on user questions. You stop relying on static datasets for your RAG pipelines.

Organize your Browserhub scrapers in LlamaIndex

Call `list_blueprints` and `get_scraper` to update your knowledge base with your current scraping logic. This keeps your agent aware of available extraction methods. Everything remains synchronized. When you add a new scraper in the portal, your agent immediately recognizes it as a valid tool for document retrieval.

Setup guide

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

It acts as a data provider for your index. You call the tools to extract content, then pass that text to your indexing pipeline.

Once you scrape and index the data, it becomes a node in your vector store. You search it using natural language through your existing agent setup.

Security is handled by your Vinkius endpoint token. The raw web data you scrape is encrypted in transit and only visible to your specific LlamaIndex index.

Absolutely. Use `list_scrapers` to retrieve a list of all your configured jobs. Your agent can then select the right scraper to populate your index.

Account data like balances are never indexed. These reside in memory only during the tool call, ensuring your financial details don't end up in your vector stores.

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