QuestionPro MCP Server for LlamaIndexGive LlamaIndex instant access to 13 tools to Check Questionpro Status, Create Survey, Get Question, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add QuestionPro as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The QuestionPro app connector for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to QuestionPro. "
"You have 13 tools available."
),
)
response = await agent.run(
"What tools are available in QuestionPro?"
)
print(response)
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.
LlamaIndex agents combine QuestionPro tool responses with indexed documents for comprehensive, grounded answers. Connect 13 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire QuestionPro into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the QuestionPro MCP Server
LlamaIndex provides unique advantages when paired with QuestionPro through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine QuestionPro tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain QuestionPro tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query QuestionPro, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what QuestionPro tools were called, what data was returned, and how it influenced the final answer
QuestionPro + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the QuestionPro MCP Server delivers measurable value.
Hybrid search: combine QuestionPro real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query QuestionPro to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying QuestionPro for fresh data
Analytical workflows: chain QuestionPro queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for QuestionPro in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting QuestionPro to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQuestionPro + LlamaIndex FAQ
Common questions about integrating QuestionPro MCP Server with LlamaIndex.