How to Use the QuestionPro MCP in LangChain

Q: Can a LangChain chain use both QuestionPro and database tools?

Yes. You can feed the output of getsurveystats directly into a database insert tool within the same LangChain execution chain. The agent handles the data transformation between the two systems.

Q: How does LangChain handle complex QuestionPro question types?

The agent calls getquestion to inspect the structural metadata of a specific question. LangChain then uses this schema to map the nested JSON response into a flat format suitable for your LLM.

Chain together raw QuestionPro feedback directly inside your LangChain agents using our MCP Server to automate post-survey analysis.

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Connect QuestionPro MCP to LangChain

Create your Vinkius account to connect QuestionPro to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup QuestionPro with LangChain

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Build multi-step LangChain feedback loops with this MCP Server

The `list_surveys` tool lets your agent find active questionnaires and feed that ID directly into `get_survey_stats` in a single LangChain execution chain. LangChain manages these tool calls sequentially, letting you pass the output of one survey check straight into the next analytical step without manual intervention. By feeding these variables into your LangChain runs, you can automatically flag low response rates with QuestionPro. The agent runs `list_responses` to pull the raw feedback, formats the data, and passes it to your downstream analysis chains for immediate processing.

Trace survey data flows with LangSmith

Calling `get_response` inside a LangChain chain triggers automatic tracing so you can inspect the exact payload your agent extracts from QuestionPro. You see the latency of each API call, the exact token cost of parsing responses, and the raw JSON structure before it hits your database. This visibility prevents silent failures in LangChain when your agent iterates through QuestionPro questions using `list_questions`. If a survey structure changes mid-campaign, your LangChain logs pinpoint exactly which field caused the parsing error.

Automate folder organization

Use `list_surveys_by_folder` within a LangChain graph to categorize QuestionPro feedback based on internal department structures. Your agent queries `list_folders` to map where surveys live, then routes the retrieved feedback to the correct team channel. This setup lets you build autonomous LangChain routing agents that inspect `list_users` to find the QuestionPro survey owner, pull the response data, and send tailored summaries to that specific user.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by QuestionPro. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about QuestionPro MCP in LangChain

You should use LangChain's built-in rate-limiting wrappers around the MCP tools. When calling `list_responses` repeatedly, configure an exponential backoff strategy in your run configuration to avoid hitting API thresholds.

Yes. You can feed the output of `get_survey_stats` directly into a database insert tool within the same LangChain execution chain. The agent handles the data transformation between the two systems.

The agent calls `get_question` to inspect the structural metadata of a specific question. LangChain then uses this schema to map the nested JSON response into a flat format suitable for your LLM.

This MCP server is stateless by default, making it easy to run inside ephemeral LangChain runtimes. If you need to persist survey IDs across multiple turns, pass them as state variables in your LangGraph state.

Your survey responses and participant details remain inside the V8 sandbox during execution. The data is processed in-memory and sent directly to your LangChain application without persistent storage on the Vinkius platform.

Use it with your favorite AI tools

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