How to Use the zrok (Open-Source Tunnel) MCP in LlamaIndex

Q: Can I use zrok (Open-Source Tunnel) with LlamaIndex to query past setups?

Yes. You index the results of setup actions like createshare. Later queries can then retrieve context, answering questions about configurations from previous sessions.

Ground RAG applications by indexing zrok (Open-Source Tunnel) data with LlamaIndex.

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Connect zrok (Open-Source Tunnel) MCP to LlamaIndex

Create your Vinkius account to connect zrok (Open-Source Tunnel) 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 zrok (Open-Source Tunnel) with LlamaIndex

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Indexing Share Status Data

Instead of just running a tool, you index the results. For instance, calling `list_shares` provides output that becomes searchable knowledge. Your RAG application can then answer questions like, 'Which private shares are currently active?' based on past data.

Querying Account Limits

The agent runs `get_account` to pull limit information into the vector store. Later queries don't just get a raw number; they get an answer grounded in that historical API data, which is far better than relying on memory.

Tracking Environment History

By indexing outputs from `list_environments` over time, you build a searchable log. You can query past sessions to understand 'When was this specific environment last enabled?' without needing complex database joins.

Setup guide

Set up zrok (Open-Source Tunnel) 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 zrok (Open-Source Tunnel) 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 zrok (Open-Source Tunnel) tools.",
)
response = await agent.run("List recent zrok (Open-Source Tunnel) data")

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Common questions about zrok (Open-Source Tunnel) MCP in LlamaIndex

You feed the tool outputs—like a list of active shares from `list_shares`—into your vector store. This turns ephemeral API data into permanent, searchable knowledge for retrieval.

Yes. You index the results of setup actions like `create_share`. Later queries can then retrieve context, answering questions about configurations from previous sessions.

The server provides account details and resource statuses. By indexing these structured results, you create a knowledge base that grounds answers in live API data, not just general text.

The MCP Server structure ensures the output from tools like `login_account` is structured and reliable. This clean input minimizes hallucinations when building your RAG application.

This server manages share configurations, environment status, and account limits. The specific data type you'll index is `Account Details` alongside resource definitions.

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