How to Use the AcademyOcean MCP in AutoGen

Q: How do I expose AcademyOcean tools to an AutoGen MCP Server agent?

You register the MCP server using mcpservertools and pass the returned tool list to your AssistantAgent. The framework handles the schema conversion automatically.

Q: How do AutoGen agents handle errors from AcademyOcean tools?

If getlearnerprogress returns an error, the executing agent posts the issue back to the group chat. The other agents then debate how to correct the input parameters.

Q: Does this setup support the Streamable HTTP transport?

Yes. You configure StreamableHttpServerParams with your Vinkius endpoint URL to connect your agents to the tools securely over the web.

Let AutoGen agents debate and coordinate AcademyOcean learner onboarding and progress tracking.

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Connect AcademyOcean MCP to AutoGen

Create your Vinkius account to connect AcademyOcean 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 AcademyOcean with AutoGen

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Coordinate training invitations via agent consensus

The `invite_learner` tool executes onboarding commands only after your AutoGen agents agree on the candidate's eligibility. A hiring agent can propose the invite, while a budget agent reviews course seat limits. This multi-agent debate prevents accidental invites and wasted seats. Once consensus is reached, the primary assistant triggers the tool to add the user to AcademyOcean.

Analyze learner progress with specialized agents

The `get_learner_progress` tool feeds real-time completion data into a conversation between your tracking and tutoring agents. One agent flags low scores, while the other drafts a custom study plan. This collaborative loop ensures that struggling students get immediate attention. The agents use the raw metrics to coordinate their response before notifying the training manager.

Manage directories with an AutoGen MCP Server agent

The `list_learners` and `list_courses` tools give your agent group complete visibility over your LMS environment. An admin agent can cross-reference these lists to identify inactive accounts. Instead of relying on a single script, the agents discuss the roster discrepancies. They decide which users need updates and execute the cleanup steps autonomously.

Setup guide

Set up AcademyOcean 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 AcademyOcean 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="AcademyOcean_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent AcademyOcean 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 AcademyOcean. 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 AcademyOcean MCP in AutoGen

You register the MCP server using `mcp_server_tools` and pass the returned tool list to your `AssistantAgent`. The framework handles the schema conversion automatically.

Yes, you can set up a group chat where a manager agent must approve the execution of `invite_learner` before the executor agent actually runs it.

If `get_learner_progress` returns an error, the executing agent posts the issue back to the group chat. The other agents then debate how to correct the input parameters.

Yes. You configure `StreamableHttpServerParams` with your Vinkius endpoint URL to connect your agents to the tools securely over the web.

All transmission occurs over encrypted TLS connections directly to your local AutoGen runtime. The Vinkius MCP gateway acts as a pass-through layer, ensuring no training data is cached or stored.

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