How to Use the LearningSuite MCP in AutoGen

Enable multi-agent debate and decision-making using the LearningSuite MCP server in AutoGen.

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Connect LearningSuite MCP to AutoGen

Create your Vinkius account to connect LearningSuite 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 LearningSuite with AutoGen

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Claude

Perplexity

Let agents debate LearningSuite data

Deploy multiple agents to review `list_ai_agents` and discuss their findings. Because AutoGen handles the conversation, your agents can challenge each other on training performance. One agent fetches the course data while the other critiques the completion rates. This negotiation leads to better decisions for your distributed teams.

Automate consensus with LearningSuite tools

Use the McpToolAdapter to give your agents direct access to the LearningSuite API. They can compare `get_member` info across different hubs to resolve discrepancies. This setup allows agents to reach a consensus on who needs follow-up training. They work through the data until they agree on the next action.

Scale your L&D logic with AutoGen

Build systems where agents negotiate training schedules based on `list_courses`. The agents handle the back-and-forth, only notifying you when they need approval. It removes the manual work of checking who is enrolled in what. Your agents handle the heavy lifting of coordinating your team's progress.

Setup guide

Set up LearningSuite 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 LearningSuite 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="LearningSuite_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent LearningSuite 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="LearningSuite_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about LearningSuite MCP in AutoGen

Use the MCP tool adapter to connect to the server and pass the list of tools to your AssistantAgent. The adapter handles the schema conversion automatically.

Yes. You can assign different roles to your agents, like a performance analyst and a security checker, and let them debate the output of the tools.

It is built for it. The server tools are compatible with the AssistantAgent constructor, allowing for deep integration into your multi-agent workflows.

You manage access at the server level via your endpoint token. Your agents only see the data you allow them to fetch through the tool definitions.

The server processes all requests in an isolated sandbox. Your sensitive member records are never stored in the agent's memory longer than the current conversation requires.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript