How to Use the Zenloop MCP in CrewAI

Q: How does Zenloop fit into a CrewAI workflow?

Zenloop provides the specialized tools (like createoverlayanswer) that your agents execute. The crew coordinates which tool runs when, passing memory between steps.

Q: What data does Zenloop expose for autonomous operations with CrewAI?

The server exposes survey definitions (listsurveys) and actual customer responses, letting your agents research the full context of the issue.

Q: Does Zenloop support filtering answers when used in CrewAI?

Yup. When listing results using listsurveyanswers, you specify date filters, which is key for keeping reports actionable and narrow.

Run autonomous teams to analyze NPS feedback using CrewAI and Zenloop's MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Zenloop MCP to CrewAI

Create your Vinkius account to connect Zenloop to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Zenloop with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Delegating Survey Response Creation

You can assign a specialized agent the task of writing responses. The `create_embed_answer` tool allows one agent to manage Website Embed survey responses, while another handles Email Embed using `create_email_answer`. The crew ensures that all necessary channels—like Link or Overlay—are accounted for when building out comprehensive response plans.

Autonomous Survey Analysis via MCP Server

A monitoring agent can call `get_survey_details` to gather the full context of a survey. Then, an analysis agent uses that data alongside `list_surveys` to determine what follow-up actions are needed. This simulates human oversight by providing all necessary metadata before any action is taken.

Gathering and Filtering Feedback Reports

The team can execute a routine where one agent retrieves account information via `get_account_details`, and another uses `list_survey_answers` to pull filtered reports. This ensures the final report is always current. It's ideal for autonomous operations that require timely data, like checking answers from only last week.

Setup guide

Set up Zenloop MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Zenloop tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Zenloop Analyst",
    goal="Access and analyze Zenloop data via MCP.",
    backstory="Expert analyst with direct Zenloop access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Zenloop transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Zenloop Analyst",
        goal="Access and analyze Zenloop data via MCP.",
        backstory="Expert analyst with direct Zenloop access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zenloop transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zenloop. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Zenloop now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Zenloop MCP in CrewAI

Zenloop provides the specialized tools (like `create_overlay_answer`) that your agents execute. The crew coordinates which tool runs when, passing memory between steps.

Yes. You can assign different roles to handle various response types—for example, one agent handles `create_link_answer` while another manages `create_embed_answer`.

The server exposes survey definitions (`list_surveys`) and actual customer responses, letting your agents research the full context of the issue.

Yup. When listing results using `list_survey_answers`, you specify date filters, which is key for keeping reports actionable and narrow.

The server touches survey responses and account information. You must manage the endpoint token to ensure only authorized agents access this data.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python