How do I find my Survey Hash ID?

Use the `list_surveys` tool to see a comprehensive list of all surveys in your account along with their unique hash IDs.

Can I filter survey responses by date?

Yes, the `list_survey_answers` tool accepts optional `date_from` and `date_to` parameters in YYYY-MM-DD format.

Is it possible to programmatically submit an NPS score?

Absolutely. You can use the specific create tools (e.g., `create_link_answer`) to submit scores and comments for any survey channel.

Zenloop MCP Server for LangChain 8 tools — connect in under 2 minutes

Name: Zenloop
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Zenloop through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "zenloop": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Zenloop, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Zenloop MCP Server

Connect your Zenloop account to any AI agent to streamline your Net Promoter System (NPS) and customer feedback management. This MCP server enables your agent to interact with surveys, responses (answers), and account metadata directly from natural language.

LangChain's ecosystem of 500+ components combines seamlessly with Zenloop through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Survey Oversight — List all your active and historical surveys and retrieve their detailed summaries
Feedback Extraction — List customer answers and responses for any survey, filtered by date range
Response Generation — Programmatically create new survey answers across Link, Email, and Website channels
Performance Monitoring — Access NPS scores and comments to track customer sentiment in real-time
Account Visibility — Retrieve high-level account configuration and metadata for your Zenloop project

The Zenloop MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Zenloop to LangChain via MCP

Follow these steps to integrate the Zenloop MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from Zenloop via MCP

Why Use LangChain with the Zenloop MCP Server

LangChain provides unique advantages when paired with Zenloop through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Zenloop MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Zenloop queries for multi-turn workflows

Zenloop + LangChain Use Cases

Practical scenarios where LangChain combined with the Zenloop MCP Server delivers measurable value.

RAG with live data: combine Zenloop tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Zenloop, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Zenloop tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Zenloop tool call, measure latency, and optimize your agent's performance

Zenloop MCP Tools for LangChain (8)

These 8 tools become available when you connect Zenloop to LangChain via MCP:

create_email_answer

Create a new survey response for an Email Embed channel

create_embed_answer

Create a new survey response for a Website Embed channel

create_link_answer

Create a new survey response for a Link channel

create_overlay_answer

Create a new survey response for a Website Overlay channel

get_account_details

Get Zenloop account information

get_survey_details

Get details for a specific survey

list_survey_answers

Can be filtered by date. List answers (responses) for a survey

list_surveys

List all configured surveys

Example Prompts for Zenloop in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Zenloop immediately.

"List all active surveys in my Zenloop account."

"Show me customer responses for survey ID 'abc123xyz' from last week."

"Submit a Link response for survey 'abc123' with score 10 and comment 'Amazing experience!'."

Troubleshooting Zenloop MCP Server with LangChain

Common issues when connecting Zenloop to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Zenloop + LangChain FAQ

Common questions about integrating Zenloop MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.