How to Use the Udemy MCP in AutoGen

Q: How can AutoGen help me evaluate a specific Udemy course?

You give one agent the courseid. That agent calls both courses() and coursereviews(courseid), then passes the combined data to a second agent for analysis.

Q: Can AutoGen manage feedback across multiple Udemy courses?

Yes. By running instructorreviews(), you gather global sentiment. Two agents can debate which specific course, identified via instructorcourses(), has the most urgent need for revision.

Q: What is the process for using Udemy and AutoGen to analyze Q&A?

You initiate a multi-agent conversation. One agent runs instructorqa() across all courses, while another checks instructormessages() history, letting them debate whether recent messages explain old questions.

Q: Do I need to know the course ID upfront using Udemy and AutoGen?

No. You can start by running instructorcourses(), which gives a list of all IDs. This initial output feeds into the debate, giving the agents scope before they hit the specific tools.

Q: What data privacy concerns should I know when using Udemy and AutoGen?

This MCP Server touches student message content. Because of this, all agent conversations must include a step where an agent explicitly filters or anonymizes private messages before final output.

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Create your Vinkius account to connect Udemy 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 Udemy with AutoGen

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Simulating review debate with MCP Server.

Set up two agents: a 'Sentiment Checker' that calls `course_reviews(course_id)` and reports on negative keywords. A second 'Strategy Agent' then uses the output to propose solutions, like adjusting course material. This simulates deliberation: one agent identifies problems with reviews, and another generates actionable responses based purely on those findings.

Comparing instructor feedback using AutoGen.

Give two agents competing roles. Agent One runs `instructor_reviews()` to gather global feedback metrics. Agent Two uses this data and combines it with the course list from `courses()` to argue which specific class needs immediate attention. The outcome is a negotiated priority list, not just raw data.

Negotiating content improvements via AutoGen.

One agent pulls all current courses using `instructor_courses()`. A second agent then calls `instructor_qa()` for *all* those courses. The agents debate which topics are most frequently questioned, converging on the top three areas needing updates. This system moves beyond simple listing; it determines the critical gaps based on high-frequency questions.

Setup guide

Set up Udemy 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 Udemy 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="Udemy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Udemy MCP in AutoGen

You give one agent the `course_id`. That agent calls both `courses()` and `course_reviews(course_id)`, then passes the combined data to a second agent for analysis.

Yes. By running `instructor_reviews()`, you gather global sentiment. Two agents can debate which specific course, identified via `instructor_courses()`, has the most urgent need for revision.

You initiate a multi-agent conversation. One agent runs `instructor_qa()` across all courses, while another checks `instructor_messages()` history, letting them debate whether recent messages explain old questions.

No. You can start by running `instructor_courses()`, which gives a list of all IDs. This initial output feeds into the debate, giving the agents scope before they hit the specific tools.

This MCP Server touches student `message content`. Because of this, all agent conversations must include a step where an agent explicitly filters or anonymizes private messages before final output.

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