How to Use the Zenloop MCP in LangChain

Q: Can LangChain manage multiple Zenloop surveys?

Yes. You can first use the listsurveys tool to get all configured survey IDs, and then loop through them to fetch answers for each one using listsurveyanswers.

Q: What data does Zenloop provide regarding account details?

The getaccountdetails tool gives your agent basic Zenloop account information. This is useful for verifying permissions or getting context before attempting to write a new answer using any of the createanswer tools.

Q: Is the MCP Server stateless when used with LangChain?

By default, it is stateless. But you can use client.session() if your multi-step agent needs to maintain context or data between separate tool calls.

Build multi-step reasoning agents with LangChain using the Zenloop MCP Server.

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Connect Zenloop MCP to LangChain

Create your Vinkius account to connect Zenloop 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 Zenloop with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Process Surveys and Responses

The `create_email_answer`, `create_embed_answer`, `create_link_answer`, and `create_overlay_answer` tools let your agent generate specific survey responses. You can route feedback to the right channel, whether it's an email embed or a simple link response.

Manage Survey Data Retrieval

Need context? Start by calling `get_account_details` and `get_survey_details`. From there, your agent can use `list_surveys` to map out what Zenloop offers. This setup lets the subsequent steps in your LangChain pipeline gather all necessary initial data.

Analyze Feedback Trends

The core reporting function is exposed through `list_survey_answers`. You can filter these responses by date, giving your agent precise temporal context for analysis. This lets the final steps of your chain analyze NPS feedback across specific time frames.

Setup guide

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

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Common questions about Zenloop MCP in LangChain

Your agent decides which function to call next based on what the previous call returned. For instance, after getting survey details with `get_survey_details`, it knows exactly how to structure a response using `create_embed_answer`.

Yes. You can first use the `list_surveys` tool to get all configured survey IDs, and then loop through them to fetch answers for each one using `list_survey_answers`.

The `get_account_details` tool gives your agent basic Zenloop account information. This is useful for verifying permissions or getting context before attempting to write a new answer using any of the `create_*_answer` tools.

By default, it is stateless. But you can use `client.session()` if your multi-step agent needs to maintain context or data between separate tool calls.

This server handles customer survey responses and account details. Specifically, it manages the raw text content found in `list_survey_answers`.

Use it with your favorite AI tools

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