How to Use the MSAAQ MCP in AutoGen

Use AutoGen and MSAAQ to create debating agents that manage student enrollment and verify academic records through consensus.

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Connect MSAAQ MCP to AutoGen

Create your Vinkius account to connect MSAAQ 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 MSAAQ with AutoGen

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Debate student enrollment decisions

Assign one agent to check availability via `list_available_learning_courses` and another to execute `enroll_student_in_course`. They discuss the enrollment requirements before acting. This setup prevents invalid enrollments by forcing agents to challenge each other's input parameters. You ensure only qualified students get registered.

Negotiate certificate validity

Create a security agent that calls `verify_issued_certificate` and a registrar agent that reviews the results. They debate the authenticity until they reach a consensus. If the agents disagree, they flag the student record for your manual review. You automate the heavy lifting while keeping a high standard for credential integrity.

Analyze course performance as a team

Let a statistics agent fetch data using `get_course_performance_stats` while another agent drafts a summary. They refine the report through multiple rounds of conversation. You get a polished analysis that accounts for different metrics. The agents reconcile any discrepancies in the data before presenting the final report.

Setup guide

Set up MSAAQ 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 MSAAQ 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="MSAAQ_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="MSAAQ_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about MSAAQ MCP in AutoGen

Use the McpToolAdapter to automatically convert the server schema. Pass the resulting tool list to your AssistantAgent constructor to enable conversation-based execution.

Yes. Agents exchange messages during the debate process. One agent can request data, and the other can use that output to perform the next step.

The server supports both stdio and Streamable HTTP. Use the StreamableHttpServerParams to point your AutoGen agents to the correct MSAAQ endpoint.

They debate. You define the system prompts for your agents to prioritize specific outcomes, like speed or accuracy, during the tool call negotiation.

All agent conversations happen within your defined environment. The MSAAQ tools only touch the API, and no sensitive user information is shared with outside parties.

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