How to Use the Wenjuanxing / 问卷星 MCP in LangChain

Q: Does using MCP Server tools with LangChain handle different types of data?

Yeah, it handles everything. Whether it's metadata from getaccountinfo or specific response counts from getstats, the output is structured so your agent can use it immediately in the next step.

Build complex data pipelines for Wenjuanxing / 问卷星 using LangChain.

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Connect Wenjuanxing / 问卷星 MCP to LangChain

Create your Vinkius account to connect Wenjuanxing / 问卷星 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 Wenjuanxing / 问卷星 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chaining MCP Server calls in LangChain

You can build multi-step processes where the output of one tool immediately feeds into the next. For example, your agent first uses `list_surveys` to find a questionnaire ID. It then passes that ID straight into `get_survey` to grab all the specific details needed for reporting.

Automating survey status changes with LangChain

Need to manage the lifecycle of a form? Your agent calls `update_survey_status` and passes in the desired action (publish or pause). This allows you to create automated workflows that change questionnaire visibility based on external triggers, like reaching a response goal.

Multi-step data retrieval for LangChain

Don't just get raw numbers. You can chain calls: use `query_surveys` to filter questionnaires by keyword first. Then, feed the resulting list of IDs into `list_responses` to gather all relevant feedback records in one automated sequence.

Setup guide

Set up Wenjuanxing / 问卷星 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 Wenjuanxing / 问卷星 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({
    "wenjuanxing-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 Wenjuanxing / 问卷星 transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Wenjuanxing / 问卷星 MCP in LangChain

LangChain lets your agent reason about the best way to find what you need. Instead of writing separate code for every task, you build a chain that decides if it should use `query_surveys` or `list_surveys` first.

Absolutely. Because every MCP tool call is a link in the chain, you get full observability. You can trace exactly which tools were called and what inputs they received when interacting with Wenjuanxing / 问卷星.

You can build a chain that iterates through several survey IDs. For each ID, the agent calls `get_report` and aggregates all those resulting summary reports into one final output for you.

Yeah, it handles everything. Whether it's metadata from `get_account_info` or specific response counts from `get_stats`, the output is structured so your agent can use it immediately in the next step.

This MCP Server touches survey response metadata. Your AI client handles authentication using an endpoint token, keeping the connection secure.

Use it with your favorite AI tools

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