How to Use the Zenedu MCP in LangChain

Q: How does Zenedu help with LangChain observability?

Zenedu exposes every tool call—like listbots or listbotsubscribers—as a distinct step in your chain. This means your agent's decision-making process is fully traceable, letting you see exactly why it decided to call that function and what the output was.

Build Multi-Step Bot Logic with LangChain and Zenedu MCP Server.

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Connect Zenedu MCP to LangChain

Create your Vinkius account to connect Zenedu 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 Zenedu with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Map the entire bot ecosystem

Need to figure out what bots you've got running? Start by calling `list_bots`. This gives your agent a roster of every messenger bot associated with your account. From that initial list, you can chain calls. For instance, if you identify Bot A, the next step is using `list_bot_subscribers` to pull all registered users for just that specific bot.

Track customer journeys and revenue

You're not just listing names; you're building a sequence. First, run `list_bot_products` to see what items are available. Then, chain that into `list_bot_orders` to track exactly which products users bought from those bots. This multi-step process lets your agent calculate total revenue per bot or find out if certain product lines generate more orders.

Verify funnel and offer structures

Want to check a specific marketing flow? Your agent runs `list_bot_funnels` first. Once the funnel is mapped, you can cross-reference it with available deals using `list_bot_offers`. This helps confirm if the current offers align with the intended user journey. It’s all about making sure the sequence of data calls correctly replicates your business logic.

Setup guide

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

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Common questions about Zenedu MCP in LangChain

Zenedu exposes every tool call—like `list_bots` or `list_bot_subscribers`—as a distinct step in your chain. This means your agent's decision-making process is fully traceable, letting you see exactly why it decided to call that function and what the output was.

Absolutely. Your agent can run a multi-step query: first find all bots using `list_bots`, then check the orders for each bot using `list_bot_orders`, and finally summarize which bots are most profitable. It links these steps together automatically.

The MCP Server gives access to operational data, including bot details, subscriber lists, funnels, and orders. Your agent treats this live API output as inputs for its next decision or action.

Yes. Because it's built for framework agents like LangChain, you get full observability on every tool call and can combine this bot data with other services in a single chain.

Zenedu handles communication messaging data. Specifically, the server exposes tools for managing bots, subscribers, funnels, and product orders within that domain.

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