How to Use the Zenedu MCP in CrewAI

Q: How does Zenedu help with messaging operations using CrewAI?

You define a crew where one agent calls listbots to get the inventory. Another specialized agent then takes that list and verifies associated data points like offers or products.

Q: What if I need to see all bot products using Zenedu with CrewAI?

You give a Research role access to listbotproducts. This agent gathers the data, and you can then have another agent review that product list against available funnels for completeness.

Autonomous Bot Operations: Running Zenedu's Messaging Tools with CrewAI Agents.

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Connect Zenedu MCP to CrewAI

Create your Vinkius account to connect Zenedu 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.

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Setup Zenedu with CrewAI

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Audit All Bots via Collaborative Agent Teams

You set up a Research agent to call `list_bots` and gather the full roster of available bots. A Monitor agent then takes this list and cross-references it against all known products using `list_bot_products`. This multi-agent approach ensures no bot is missed and provides immediate visibility into the entire system's assets.

Automate Subscription Status Checks

A dedicated Analyst agent runs `list_bot_subscribers`. This result is then passed to a Moderator agent, which formats the data and generates an executive summary of user growth. The specialized roles prevent single points of failure; if subscriber data retrieval stalls, the rest of the crew can continue analyzing other available tools.

Cross-Reference Orders with Funnels

The workflow starts by having an Action agent pull all order details using `list_bot_orders`. The results are then analyzed against known funnels via `list_bot_funnels` to identify conversion drop-off points. This is superior to single-script methods because the specialized agents handle complex data joins and reporting automatically.

Setup guide

Set up Zenedu 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 Zenedu tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="Zenedu Analyst",
        goal="Access and analyze Zenedu data via MCP.",
        backstory="Expert analyst with direct Zenedu access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zenedu 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 Zenedu. 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 Zenedu MCP in CrewAI

You define a crew where one agent calls `list_bots` to get the inventory. Another specialized agent then takes that list and verifies associated data points like offers or products.

Yes. You assign an Analyst role in your crew, giving it access to the `list_bot_subscribers` tool. The specialized agent executes the call and shares memory with other agents for follow-up analysis.

You give a Research role access to `list_bot_products`. This agent gathers the data, and you can then have another agent review that product list against available funnels for completeness.

It is. You set up an autonomous operation where one agent pulls all offers using `list_bot_offers`. This output can then be automatically used by a second agent to recommend changes.

CrewAI operates on operational datasets, specifically bot names (`list_bots`), funnels definitions using `list_bot_funnels`, and user transaction records via `list_bot_orders`.

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