How to Use the Zenloop MCP in LlamaIndex

Q: How do I query past survey answers with LlamaIndex and Zenloop?

You pass the listsurveyanswers tool into your FunctionAgent. Then, you can query it later to retrieve historical data points that become part of your searchable knowledge base.

Index Zenloop data into a knowledge base with LlamaIndex for grounded AI answers.

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Connect Zenloop MCP to LlamaIndex

Create your Vinkius account to connect Zenloop to LlamaIndex 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 Zenloop with LlamaIndex

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Build Knowledge from Survey Answers

The `list_survey_answers` tool allows your RAG application to pull raw customer feedback. By indexing this data, you can query past responses and get context-aware answers grounded in actual Zenloop API results.

Retrieve Configuration Details

Need background info? Use `get_survey_details` or `list_surveys` to pull structured data into your index. This means you can ask, 'What are the settings for Survey X?' and get a precise, indexed answer.

Simulate Response Creation

While not writing to live systems, indexing knowledge about response creation is useful. You can process information from `create_email_answer` or `create_overlay_answer` tool definitions into your index. This allows the agent to explain *how* a response should be structured.

Setup guide

Set up Zenloop 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 Zenloop 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 Zenloop tools.",
)
response = await agent.run("List recent Zenloop data")

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Common questions about Zenloop MCP in LlamaIndex

It indexes the results from the MCP Server into a vector store. This means the AI client can perform semantic search on things like survey answers, giving you context beyond simple keyword matching.

Yes, it supports combining different tool specifications and resources into one unified index. You're not limited to just the data coming from this MCP Server alone.

You pass the `list_survey_answers` tool into your FunctionAgent. Then, you can query it later to retrieve historical data points that become part of your searchable knowledge base.

The key advantage is grounding. Instead of generating plausible-sounding text, the agent retrieves answers directly from documented API calls like `get_account_details` or `list_surveys`.

This server handles customer survey responses and account details. The indexed data includes raw text content from the `list_survey_answers` tool.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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