QuestionPro MCP Server for LangChainGive LangChain instant access to 13 tools to Check Questionpro Status, Create Survey, Get Question, and more
LangChain is the leading Python framework for composable LLM applications. Connect QuestionPro through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The QuestionPro app connector for LangChain is a standout in the Data Analytics category — giving your AI agent 13 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"questionpro": {
"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 QuestionPro, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 QuestionPro MCP Server
Bring advanced survey analytics into your AI workflow with QuestionPro. Your agents can orchestrate end-to-end feedback loops by filtering folders for active campaigns, compiling real-time response statistics, retrieving granular participant data, and maintaining contact lists—all executed conversationally.
LangChain's ecosystem of 500+ components combines seamlessly with QuestionPro through native MCP adapters. Connect 13 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
- Create, retrieve, and organize surveys with folder filtering
- Analyze real-time survey statistics and completion rates
- Collect and inspect individual respondent data
- Manage question banks and user administration
- Organize email outreach lists efficiently
Who is it for?
Ideal for market researchers, HR teams, and product managers needing fast, AI-driven insights from customer and employee feedback.The QuestionPro MCP Server exposes 13 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.
All 13 QuestionPro tools available for LangChain
When LangChain connects to QuestionPro through Vinkius, your AI agent gets direct access to every tool listed below — spanning market-research, customer-feedback, employee-engagement, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify connectivity
Create a survey
Get question details
Get response details
Get survey details
Get survey statistics
List email lists
List folders
List survey questions
List survey responses
List surveys
List surveys by folder
List account users
Connect QuestionPro to LangChain via MCP
Follow these steps to wire QuestionPro into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the QuestionPro MCP Server
LangChain provides unique advantages when paired with QuestionPro through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine QuestionPro 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 QuestionPro queries for multi-turn workflows
QuestionPro + LangChain Use Cases
Practical scenarios where LangChain combined with the QuestionPro MCP Server delivers measurable value.
RAG with live data: combine QuestionPro tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query QuestionPro, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain QuestionPro tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every QuestionPro tool call, measure latency, and optimize your agent's performance
Example Prompts for QuestionPro in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with QuestionPro immediately.
"Show response rate and completion stats for our customer satisfaction survey"
"Show me all active surveys with their response rates and completion percentages."
"Export the detailed analytics report for the Customer Experience 2025 survey."
Troubleshooting QuestionPro MCP Server with LangChain
Common issues when connecting QuestionPro to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersQuestionPro + LangChain FAQ
Common questions about integrating QuestionPro MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.