How to Use the LearnWorlds MCP in AutoGen

Q: How do I register LearnWorlds MCP tools with AutoGen agents?

Use the mcpservertools helper with your Vinkius connection parameters to fetch the tool definitions. Pass this list directly to your AssistantAgent constructor to make them available.

Deploy multi-agent debates in AutoGen to manage LearnWorlds course enrollments and billing audits.

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Connect LearnWorlds MCP to AutoGen

Create your Vinkius account to connect LearnWorlds to AutoGen 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 AutoGen

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Multi-Agent Debates with AutoGen

This MCP Server exposes `create_student` and `enroll_student` to enable multi-agent decision workflows in your system. In AutoGen, a registration agent can draft a new student profile while a validator agent checks for duplicate emails. They discuss the payload details before executing the creation tool to prevent database corruption. This consensus-driven approach prevents duplicate accounts and bad formatting. The agents negotiate the correct parameters in the chat thread before committing the API write. You get clean, validated user data without writing custom validation scripts.

Audit Payments with Competing Agents

The `list_payments` tool allows a financial agent to fetch transaction records while a subscription agent calls `list_subscriptions`. These two agents compare notes in a structured conversation to flag accounts that have active access without a matching payment. They debate the discrepancies and output a clean list of accounts to freeze. This architecture separation ensures that security policies are strictly enforced. The financial agent focuses purely on ledger balance, while the subscription agent focuses on user access. Their debate converges on an accurate list of billing exceptions.

Automated Curriculum Audits via AutoGen

Using `list_courses` and `get_course_contents` allows a curriculum agent to extract course trees and debate quality compliance with an editorial agent. The curriculum agent fetches the structure, while the editorial agent checks for empty modules or missing lessons. They collaborate to generate a detailed quality report of your school. The conversation history provides a clear audit trail of why certain courses were flagged. You see the step-by-step reasoning that led to the final edit suggestions. Your agents handle the API coordination entirely through conversational turn-taking.

Setup guide

Set up LearnWorlds MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes LearnWorlds tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="LearnWorlds_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent LearnWorlds data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="LearnWorlds_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent LearnWorlds data")
    print(result.messages[-1].content)

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 AutoGen

Use the `mcp_server_tools` helper with your Vinkius connection parameters to fetch the tool definitions. Pass this list directly to your AssistantAgent constructor to make them available.

Yes, the framework supports parallel tool execution. If your agents decide to fetch details for multiple users, they can trigger several `get_student_details` calls simultaneously using the MCP integration.

The agents discuss the tool outputs in their chat history. If `enroll_student` returns an error, the coordinator agent asks the registration agent to fix the payload and try again.

The integration supports both stdio and Streamable HTTP transports. Vinkius provides a secure HTTP endpoint that handles token authentication automatically.

Your student profiles and registration details are processed within ephemeral V8 sandboxes. Vinkius enforces a zero-trust model, ensuring that payload data is never written to disk or exposed to other tenants.

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