How to Use the Hubilo MCP in LangChain

Q: Can LangChain perform multiple Hubilo operations in one run?

Yes. The agent evaluates the state and triggers tools like listorders or listtickets in sequence as required by the task.

Build event automation chains in LangChain by linking Hubilo API data directly to your agent's reasoning process.

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Connect Hubilo MCP to LangChain

Create your Vinkius account to connect Hubilo 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 Hubilo with LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain event data for autonomous agents

Connect your agent to `list_events` and `get_event_details` to build pipelines that trigger actions based on live event status. You define the logic, and the agent executes the sequence without manual oversight. Your agent parses the output of `list_sessions` and `list_speakers` to build dynamic itineraries. It treats each API response as a step in a chain, ensuring your workflow stays grounded in real-time data.

Pipeline booth analytics with LangChain

Feed `get_booth_analytics` directly into your LangGraph nodes to generate automated performance reports. By chaining this tool with other data sources, you turn raw numbers into actionable summaries. You avoid the overhead of manual data entry by letting the agent query `list_booth_categories` when it needs context. It keeps the entire pipeline moving through logical, tool-driven steps.

Manage attendee registration flows

Automate how your agent handles attendee lists by invoking `list_attendees` within a LangChain graph. It filters and sorts user data based on the criteria you specify in your prompt. This MCP Server provides the raw input needed for your agent to verify registration status or extract contact details. You get a clean, structured output ready for the next node in your chain.

Setup guide

Set up Hubilo 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 Hubilo 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({
    "hubilo-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 Hubilo transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Hubilo MCP in LangChain

Install the MCP adapters and point your client to the endpoint. Your agent will then see the full list of available tools automatically.

Yes. The agent evaluates the state and triggers tools like `list_orders` or `list_tickets` in sequence as required by the task.

The client is stateless by default. Use a session object if you need to maintain context across multiple tool calls.

Errors bubble up through the tool execution layer. You can use standard try-catch blocks or agentic error handling to manage them.

Your data remains within the ephemeral sandbox provided by the MCP protocol. No attendee registration records are stored outside your local execution environment.

Use it with your favorite AI tools

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