Zenloop MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zenloop as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Zenloop. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Zenloop?"
)
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 Zenloop MCP Server
Connect your Zenloop account to any AI agent to streamline your Net Promoter System (NPS) and customer feedback management. This MCP server enables your agent to interact with surveys, responses (answers), and account metadata directly from natural language.
LlamaIndex agents combine Zenloop tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Survey Oversight — List all your active and historical surveys and retrieve their detailed summaries
- Feedback Extraction — List customer answers and responses for any survey, filtered by date range
- Response Generation — Programmatically create new survey answers across Link, Email, and Website channels
- Performance Monitoring — Access NPS scores and comments to track customer sentiment in real-time
- Account Visibility — Retrieve high-level account configuration and metadata for your Zenloop project
The Zenloop MCP Server exposes 8 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.
How to Connect Zenloop to LlamaIndex via MCP
Follow these steps to integrate the Zenloop MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Zenloop
Why Use LlamaIndex with the Zenloop MCP Server
LlamaIndex provides unique advantages when paired with Zenloop through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zenloop tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zenloop tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zenloop, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zenloop tools were called, what data was returned, and how it influenced the final answer
Zenloop + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zenloop MCP Server delivers measurable value.
Hybrid search: combine Zenloop real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zenloop 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 Zenloop for fresh data
Analytical workflows: chain Zenloop queries with LlamaIndex's data connectors to build multi-source analytical reports
Zenloop MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Zenloop to LlamaIndex via MCP:
create_email_answer
Create a new survey response for an Email Embed channel
create_embed_answer
Create a new survey response for a Website Embed channel
create_link_answer
Create a new survey response for a Link channel
create_overlay_answer
Create a new survey response for a Website Overlay channel
get_account_details
Get Zenloop account information
get_survey_details
Get details for a specific survey
list_survey_answers
Can be filtered by date. List answers (responses) for a survey
list_surveys
List all configured surveys
Example Prompts for Zenloop in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zenloop immediately.
"List all active surveys in my Zenloop account."
"Show me customer responses for survey ID 'abc123xyz' from last week."
"Submit a Link response for survey 'abc123' with score 10 and comment 'Amazing experience!'."
Troubleshooting Zenloop MCP Server with LlamaIndex
Common issues when connecting Zenloop to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenloop + LlamaIndex FAQ
Common questions about integrating Zenloop MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zenloop with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zenloop to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.