How to Use the Browserbear MCP in LlamaIndex

Q: How does LlamaIndex ingest data from Browserbear?

The McpToolSpec converts tools like getrun into executable Python functions. Your agent runs the task, retrieves the JSON payload, and loads it into a Document object for indexing.

Q: Can I use Browserbear with LlamaIndex query engines?

Yes. You can register tools like takescreenshot or runtask with a FunctionAgent, letting the query engine decide when to fetch fresh web data.

Q: Can my agent delete completed runs to save space?

Yes, your agent can call deleterun to clean up old execution records after it has successfully indexed the scraped text.

Index Browserbear automation outputs into your LlamaIndex RAG pipelines.

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Connect Browserbear MCP to LlamaIndex

Create your Vinkius account to connect Browserbear 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 Browserbear with LlamaIndex

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Feed Live Scraped Data into LlamaIndex Vector Stores

The Browserbear MCP Server lets your RAG pipeline run browser automations on demand. Stop relying on outdated static documents when your system can fetch live data from target websites. Once the run finishes, the tool output is parsed and indexed. Your agent uses `get_run` to retrieve the extracted text, converting raw web data into searchable vector nodes immediately.

Query Past Browser Runs with Semantic Search

Querying past executions with this MCP Server lets you pull historical web data directly into your index. You don't need to write manual database queries to find historical web data. Let your agent call `list_runs` to pull a log of all execution metadata. The agent can then inspect individual run details using `get_run` and index the results.

Dynamically Select Automation Templates

This MCP Server lets your index-backed agent find the exact automation template it needs. Avoid hardcoding your scraper IDs into your application logic. If a user asks for a report on a specific site, the agent searches the task list using `list_tasks` and `list_projects`. It then identifies the correct template and triggers it using `run_task` without human intervention.

Setup guide

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

The McpToolSpec converts tools like `get_run` into executable Python functions. Your agent runs the task, retrieves the JSON payload, and loads it into a Document object for indexing.

Yes. You can register tools like `take_screenshot` or `run_task` with a FunctionAgent, letting the query engine decide when to fetch fresh web data.

No. Vinkius handles the authentication. Your LlamaIndex application connects using a single secure endpoint token, keeping your API keys hidden.

Yes, your agent can call `delete_run` to clean up old execution records after it has successfully indexed the scraped text.

The MCP Server processes your browser screenshots, run records, and account statistics in a zero-trust, ephemeral sandbox. No data is cached or stored permanently outside of Browserbear's own secure API.

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