How to Use the QuestionPro MCP in LlamaIndex

Q: How do I index QuestionPro survey data into LlamaIndex?

You load the data using listresponses and wrap the output in LlamaIndex Document nodes. These nodes are then passed directly to your vector store index for semantic search.

Q: Can LlamaIndex query QuestionPro metadata like folders?

Yes. The agent uses listfolders to retrieve your organizational structure and applies those folder names as metadata filters in your LlamaIndex queries.

Q: Does LlamaIndex support real-time updates from QuestionPro?

You can set up a query loop that checks getsurveystats for new completions. When the count increases, LlamaIndex pulls the new data via listresponses and updates the index.

Q: How do I filter tools when using this server with LlamaIndex?

Use the allowedtools filter in your MCP client initialization to restrict LlamaIndex to reading tools like getsurvey and disable write tools like createsurvey.

Index live QuestionPro survey responses into LlamaIndex vector stores using this MCP Server for accurate semantic search.

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Setup QuestionPro with LlamaIndex

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The `list_responses` tool pulls raw feedback data from QuestionPro and formats it into LlamaIndex Document objects for indexing. This lets you run semantic searches over thousands of customer comments without manual CSV exports. By converting live QuestionPro responses into indexable LlamaIndex nodes, your agent can answer complex qualitative questions. It queries the vector store to find patterns in feedback, avoiding the hallucination risks common with static LLM prompts.

Map survey structures to vector nodes

The `list_questions` tool extracts the exact text of your QuestionPro surveys to use as metadata in your LlamaIndex vector store. This ensures that when you search through responses, the context of the original question remains attached to the vector node. Your LlamaIndex application can then filter search results by specific QuestionPro queries using `get_question`. This targeted retrieval keeps your context windows small and your token costs low.

Query survey statistics semantically

The `get_survey_stats` tool provides real-time completion rates and response counts that LlamaIndex can query directly from QuestionPro. Instead of reading raw logs, your agent inspects these stats to decide if a survey has enough data to index. If a QuestionPro folder has new entries, the LlamaIndex agent uses `list_surveys_by_folder` to find them and trigger an update of your vector index.

Setup guide

Set up QuestionPro MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all QuestionPro MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to QuestionPro tools.",
)
response = await agent.run("List recent QuestionPro data")

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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 LlamaIndex

You load the data using `list_responses` and wrap the output in LlamaIndex Document nodes. These nodes are then passed directly to your vector store index for semantic search.

Yes. The agent uses `list_folders` to retrieve your organizational structure and applies those folder names as metadata filters in your LlamaIndex queries.

You can set up a query loop that checks `get_survey_stats` for new completions. When the count increases, LlamaIndex pulls the new data via `list_responses` and updates the index.

Use the `allowed_tools` filter in your MCP client initialization to restrict LlamaIndex to reading tools like `get_survey` and disable write tools like `create_survey`.

All survey metadata and folder structures retrieved via this MCP Server are processed in an isolated, ephemeral V8 sandbox. No survey data is cached or stored outside your designated LlamaIndex vector database.

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