Browserless MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Browserless as an MCP tool provider through the 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 Browserless. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Browserless?"
)
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 Browserless MCP Server
Connect your Browserless.io account to any AI agent and orchestrate your headless Chrome operations, web automation, and document generation through natural conversation.
LlamaIndex agents combine Browserless tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the 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
- Visual Captures — Take high-quality screenshots of any URL with advanced options like full-page capture.
- Document Generation — Convert any web page into a polished PDF document directly from your workspace.
- Rendered Content — Retrieve the fully rendered HTML content of JavaScript-heavy websites.
- Custom Scraping — Run targeted scraping requests by providing element selectors to extract specific data.
- Infrastructure Monitoring — Monitor active sessions, retrieve usage statistics, and check the health of the Browserless service.
- Configuration Access — Access and verify your account configuration and limits using natural language.
The Browserless MCP Server exposes 8 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 Browserless to LlamaIndex via MCP
Follow these steps to integrate the Browserless 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 8 tools from Browserless
Why Use LlamaIndex with the Browserless MCP Server
LlamaIndex provides unique advantages when paired with Browserless through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Browserless tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Browserless tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Browserless, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Browserless tools were called, what data was returned, and how it influenced the final answer
Browserless + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Browserless MCP Server delivers measurable value.
Hybrid search: combine Browserless real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Browserless 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 Browserless for fresh data
Analytical workflows: chain Browserless queries with LlamaIndex's data connectors to build multi-source analytical reports
Browserless MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Browserless to LlamaIndex via MCP:
check_system_health
Check the health of the Browserless service
generate_pdf
Generate a PDF of a URL
get_account_config
Retrieve account configuration
get_page_content
Retrieve the rendered HTML content of a URL
get_usage_stats
Retrieve account usage statistics
list_active_sessions
List currently active browser sessions
run_scrape
Run a custom scraping script
take_screenshot
Take a screenshot of a URL using headless Chrome
Example Prompts for Browserless in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Browserless immediately.
"Take a full-page screenshot of https://news.ycombinator.com."
"Generate a PDF of the article at https://example.com/blog/post-1."
"Scrape the titles of all products on https://example.com/shop."
Troubleshooting Browserless MCP Server with LlamaIndex
Common issues when connecting Browserless to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBrowserless + LlamaIndex FAQ
Common questions about integrating Browserless 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 Browserless 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 Browserless to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.