Browserhub 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 Browserhub as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Browserhub. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Browserhub?"
)
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 Browserhub MCP Server
Connect your Browserhub.io account to any AI agent and orchestrate your web scraping, data extraction, and proxy management workflows through natural conversation.
LlamaIndex agents combine Browserhub 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
- Scraper Oversight — List all your configured scrapers and blueprints, and retrieve detailed metadata for each.
- Job Execution — Trigger scraping jobs with dynamic URL overrides and monitor their progress in real-time.
- Direct Scraping — Perform one-off URL extractions using real browsers without pre-defined scrapers.
- Data Retrieval — Retrieve structured data captured by your jobs directly into your workspace.
- Infrastructure Management — List available proxy locations and check your account credit balance.
- Task History — List and inspect all your previous scraping jobs.
The Browserhub 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 Browserhub to LlamaIndex via MCP
Follow these steps to integrate the Browserhub 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 Browserhub
Why Use LlamaIndex with the Browserhub MCP Server
LlamaIndex provides unique advantages when paired with Browserhub through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Browserhub tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Browserhub tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Browserhub, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Browserhub tools were called, what data was returned, and how it influenced the final answer
Browserhub + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Browserhub MCP Server delivers measurable value.
Hybrid search: combine Browserhub real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Browserhub 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 Browserhub for fresh data
Analytical workflows: chain Browserhub queries with LlamaIndex's data connectors to build multi-source analytical reports
Browserhub MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Browserhub to LlamaIndex via MCP:
direct_scrape
Perform a one-off URL scrape without a pre-defined scraper
get_account_balance
Check account credit balance
get_blueprint
Get details of a specific blueprint
get_scraper
Get details of a specific scraper
get_scraping_job
Get status and results of a scraping job
list_blueprints
List all scraper blueprints
list_proxy_locations
List all available proxy locations
list_scrapers
List all configured scrapers
list_scraping_jobs
List all scraping jobs
run_scraper
Start a scraping job using a specific scraper
Example Prompts for Browserhub in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Browserhub immediately.
"List all my configured scrapers in Browserhub."
"Scrape the URL https://example.com using the 'E-commerce' scraper."
"Check my Browserhub account credit balance."
Troubleshooting Browserhub MCP Server with LlamaIndex
Common issues when connecting Browserhub to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBrowserhub + LlamaIndex FAQ
Common questions about integrating Browserhub 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 Browserhub 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 Browserhub to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.