Browse AI 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 Browse AI as an MCP tool provider through 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 Browse AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Browse AI?"
)
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 Browse AI MCP Server
Connect your Browse AI account to any AI agent and orchestrate your web scraping, data extraction, and website monitoring workflows through natural conversation.
LlamaIndex agents combine Browse AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Robot Oversight — List all your approved robots and retrieve detailed metadata for each scraper.
- Task Execution — Trigger robot runs (tasks) on specific URLs and monitor their progress in real-time.
- Data Retrieval — Retrieve structured data captured by your robots directly into your workspace.
- Website Monitoring — List and create monitor schedules to track changes on any website automatically.
- Bulk Operations — Manage and inspect bulk runs to extract data from multiple sources at once.
- System Status — Check the health and queue status of the Browse AI infrastructure.
The Browse AI 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 Browse AI to LlamaIndex via MCP
Follow these steps to integrate the Browse AI 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 Browse AI
Why Use LlamaIndex with the Browse AI MCP Server
LlamaIndex provides unique advantages when paired with Browse AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Browse AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Browse AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Browse AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Browse AI tools were called, what data was returned, and how it influenced the final answer
Browse AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Browse AI MCP Server delivers measurable value.
Hybrid search: combine Browse AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Browse AI 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 Browse AI for fresh data
Analytical workflows: chain Browse AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Browse AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Browse AI to LlamaIndex via MCP:
create_monitor
Create a new monitor schedule for a robot
get_bulk_run
Get details of a specific bulk run
get_robot
Get details of a specific robot
get_system_status
Check Browse AI system and queue status
get_task
Get status and extracted data for a task
list_bulk_runs
List all bulk runs for a robot
list_monitors
List all monitors for a specific robot
list_robots
List all approved robots
list_tasks
List all tasks for a specific robot
run_robot
Run a robot to extract data (creates a task)
Example Prompts for Browse AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Browse AI immediately.
"List all my approved web scraping robots."
"Run robot rob_123 on https://example.com/product."
"Retrieve the data from task task_99283."
Troubleshooting Browse AI MCP Server with LlamaIndex
Common issues when connecting Browse AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBrowse AI + LlamaIndex FAQ
Common questions about integrating Browse AI 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 Browse AI 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 Browse AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.