Zenloop MCP Server for OpenAI Agents SDK 8 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Zenloop through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Zenloop Assistant",
instructions=(
"You help users interact with Zenloop. "
"You have access to 8 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Zenloop"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 8 tools from Zenloop through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Zenloop, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Zenloop MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 8 tools from Zenloop
Why Use OpenAI Agents SDK with the Zenloop MCP Server
OpenAI Agents SDK provides unique advantages when paired with Zenloop through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Zenloop + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Zenloop MCP Server delivers measurable value.
Automated workflows: build agents that query Zenloop, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Zenloop, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Zenloop tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Zenloop to resolve tickets, look up records, and update statuses without human intervention
Zenloop MCP Tools for OpenAI Agents SDK (8)
These 8 tools become available when you connect Zenloop to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Zenloop to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Zenloop + OpenAI Agents SDK FAQ
Common questions about integrating Zenloop MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.