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

Q: What data does zrok (Open-Source Tunnel) provide to LangChain?

The server provides account details and limits via getaccount, plus the specific status of shares or environments. You get structured data confirming whether a resource exists, which is crucial for multi-step reasoning.

Q: How does zrok (Open-Source Tunnel) handle authentication in LangChain?

The process starts with loginaccount. The token received from that call then fuels subsequent tool calls like createshare for the rest of the chain execution. It's a mandatory first step.

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Connect zrok (Open-Source Tunnel) MCP to LangChain

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

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage Tunnels for the MCP Server

The agent calls `login_account` first to get an active token. Then, it uses that context to execute actions like calling `create_share` or checking status with `get_share`. This lets your multi-step pipeline manage external resource access reliably.

Monitor Tunnel Environments

You can build a chain that first calls `list_environments` to check which services are active. Next, it determines if an environment needs updating by calling `get_account`. This sequence lets the agent decide if the tunnel setup is correct before proceeding with core logic.

Create and Delete Shares

A simple action like making a temporary data feed available works in multiple steps. The chain first executes `create_share` to establish the connection type. Later, when done, it calls `delete_share`, ensuring all resources are cleaned up automatically.

Setup guide

Set up zrok (Open-Source Tunnel) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes zrok (Open-Source Tunnel) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "zrok-open-source-tunnel-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent zrok (Open-Source Tunnel) transactions"
    })
    print(result["messages"][-1].content)

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

LangChain treats the MCP Server tools as callable functions within its chains. This means your agent can decide, based on intermediate results, whether it needs to call `list_shares` or `get_account`. It’s just another step in a larger process.

Absolutely. You can build a flow where one agent checks the status using `list_environments`, and if it fails, another agent triggers remediation by calling `enable_environment`. This makes your deployment logic highly observable.

The server provides account details and limits via `get_account`, plus the specific status of shares or environments. You get structured data confirming whether a resource exists, which is crucial for multi-step reasoning.

The process starts with `login_account`. The token received from that call then fuels subsequent tool calls like `create_share` for the rest of the chain execution. It's a mandatory first step.

This server manages share configurations, account limits, and environment status. Specifically, it handles `Account Details` and resource definitions like active shares or environments.

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