How to Use the LearnWorlds MCP in CrewAI

Deploy a specialized crew of agents to manage your LearnWorlds operations and student lifecycle autonomously.

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Connect LearnWorlds MCP to CrewAI

Create your Vinkius account to connect LearnWorlds 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 LearnWorlds with CrewAI

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Perplexity

Autonomous student support

Assign a 'Support Agent' to use `get_student_details` when a query comes in. It gathers the facts before escalating to a human, saving your team time. Your agents act as the first line of defense for basic account questions.

Operations monitoring

A 'Monitor Agent' can run `list_courses` and `list_students` on a schedule. It watches for anomalies and reports back if something looks off. It runs in the background, so you only get pinged when there is an actual issue to address.

Collaborative enrollment crews

Set up one agent to research student history and another to execute `enroll_student`. They share the context so the final action is always informed. This team-based approach prevents errors that happen when agents act in isolation.

Setup guide

Set up LearnWorlds 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 LearnWorlds tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent LearnWorlds 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="LearnWorlds Analyst",
        goal="Access and analyze LearnWorlds data via MCP.",
        backstory="Expert analyst with direct LearnWorlds access.",
        tools=mcp_tools,
    )

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

You pass the Vinkius URL into the agent's MCP configuration. The agents will then see the tools and start collaborating immediately.

They can if you give them the tools. You control exactly which actions, like `create_student`, are available to your agents.

Yes. Agents share the tool results, so the enrollment agent knows exactly what the research agent found about the student.

Use the tool filter in your MCP server configuration. This lets you limit specific agents to read-only access while others can perform write operations.

The server uses isolated memory for your sessions. Your student names and email addresses are processed through secure channels and never shared between different crews.

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