How to Use the SurveySparrow MCP in LlamaIndex

Q: How does LlamaIndex integrate SurveySparrow data into a searchable index?

You pass the output from tools like listsurveyresponses or listcrmcontacts to your vector store. The system indexes this raw API data, meaning you can query it using natural language rather than writing database filters.

Q: Can LlamaIndex use SurveySparrow to retrieve past user onboarding details?

Yes. By indexing the results of getsurveyquestions and combining them with historical contact records, you build a knowledge base that answers questions about previous survey requirements.

Q: How is data privacy managed when using LlamaIndex with SurveySparrow?

When indexing the results of getcontactinfo, the index stores the necessary context, but you maintain control over what gets ingested. The knowledge base grounds answers in the specific API data retrieved from SurveySparrow.

Q: What if I need to check past survey invitations using LlamaIndex?

You can index the results of listemailshares. This lets you query, for example, 'When was the last major invitation sent out?' without having to manually sift through logs.

Q: Does SurveySparrow support querying its own history with LlamaIndex?

Yes. By indexing outputs from multiple listing tools (listsurveys, listcontactfolders), you create a unified knowledge source that answers questions across your entire SurveySparrow setup.

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Setup SurveySparrow with LlamaIndex

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Indexing Past Submissions for LlamaIndex

The `list_survey_responses` tool fetches every submission record. When using LlamaIndex, this output is indexed into a knowledge base. You can then query past results semantically—for example, asking 'What were the top complaints about onboarding in Q2?' without complex filtering. This turns raw data listings into actionable, searchable knowledge for your RAG applications.

Querying Account Configurations via MCP Server

If you need to know how a survey was originally set up, LlamaIndex can index the results of `get_survey_details`. You query this index to retrieve specific fields and settings. This is far better than just reading documentation; it uses your actual system data. It helps answer questions like 'What were the original objectives for Survey X?'

Tracking Contact Lifecycle History

The combination of `create_contact`, `remove_contact`, and `get_contact_info` allows LlamaIndex to build a comprehensive history index. Instead of just seeing the current status, you can query the historical state—for instance, 'When was this user last active?' or 'What folders did they belong to?' This builds deep context for your knowledge base.

Setup guide

Set up SurveySparrow 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 SurveySparrow 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 SurveySparrow tools.",
)
response = await agent.run("List recent SurveySparrow 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 SurveySparrow. 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 SurveySparrow MCP in LlamaIndex

You pass the output from tools like `list_survey_responses` or `list_crm_contacts` to your vector store. The system indexes this raw API data, meaning you can query it using natural language rather than writing database filters.

Yes. By indexing the results of `get_survey_questions` and combining them with historical contact records, you build a knowledge base that answers questions about previous survey requirements.

When indexing the results of `get_contact_info`, the index stores the necessary context, but you maintain control over what gets ingested. The knowledge base grounds answers in the specific API data retrieved from SurveySparrow.

You can index the results of `list_email_shares`. This lets you query, for example, 'When was the last major invitation sent out?' without having to manually sift through logs.

Yes. By indexing outputs from multiple listing tools (`list_surveys`, `list_contact_folders`), you create a unified knowledge source that answers questions across your entire SurveySparrow setup.

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