How to Use the Browse AI MCP in LlamaIndex

Q: How do I index Browse AI extraction results in LlamaIndex?

You use the gettaskdata tool to retrieve the structured JSON payload. LlamaIndex wraps this data into Document objects, which are then embedded and stored in your vector database.

Q: Can LlamaIndex check if a Browse AI bulk run is complete?

Yes, the agent calls getbulktask to check the status of active runs. Once the status shows as successful, it triggers the indexing step to ingest the new data.

Q: Does LlamaIndex support monitoring site changes with Browse AI?

Yes, you can use listmonitors to find active monitoring jobs. This lets your RAG pipeline know which sources are automatically updating in the background.

Turn live web scrapes from Browse AI into searchable vector indexes using this MCP Server.

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Connect Browse AI MCP to LlamaIndex

Create your Vinkius account to connect Browse AI 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 Browse AI with LlamaIndex

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Direct RAG Data Ingestion

The `get_task_data` tool pulls raw JSON results from your completed extraction tasks directly into your LlamaIndex documents. Instead of parsing HTML, your pipeline indexes clean, structured fields immediately. Your agent can query these indexed documents to answer user questions with real-time web data. This bypasses outdated training data by grounding your RAG system in fresh extractions.

Automated Bulk Indexing

The `download_bulk_data` tool fetches complete datasets from concurrent runs across dozens of target sites. Your pipeline can trigger this after running `run_bulk_task` to gather massive amounts of domain-specific data. LlamaIndex then splits, embeds, and indexes this bulk data into your vector store. It creates a highly specific, searchable knowledge base from live web sources in minutes.

Scraping Robot Configuration Inspection

The `get_robot` tool retrieves the exact schema and settings through this MCP Server. This lets your LlamaIndex agent understand what fields are available before it runs an extraction. By analyzing the robot's structure, the agent can decide whether to trigger `run_robot` or query existing tasks. This metadata helps your system map incoming user queries to the right scraping targets.

Setup guide

Set up Browse AI 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 Browse AI 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 Browse AI tools.",
)
response = await agent.run("List recent Browse AI 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 Browse AI. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

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Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Browse AI MCP in LlamaIndex

You use the `get_task_data` tool to retrieve the structured JSON payload. LlamaIndex wraps this data into Document objects, which are then embedded and stored in your vector database.

Yes, the agent calls `get_bulk_task` to check the status of active runs. Once the status shows as successful, it triggers the indexing step to ingest the new data.

Yes, you can use `list_monitors` to find active monitoring jobs. This lets your RAG pipeline know which sources are automatically updating in the background.

You install the LlamaIndex MCP tool spec and pass our endpoint URL to the client. This exposes the entire suite of scraping tools directly to your FunctionAgent.

Yes, all scraped JSON payloads and robot configurations stay inside your secure execution environment. We use ephemeral, zero-trust sandboxes to run the MCP Server, meaning your data is never cached or stored on our servers.

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