How to Use the Moodle MCP in AutoGen

Q: How do AutoGen agents coordinate course creation in Moodle?

One agent checks categories using listcategories, while another drafts the course details. Once they agree, the execution agent calls createcourses.

Q: Can I use AutoGen to audit student grades in Moodle?

Yes. An advisor agent can call getusergrades to retrieve scores, while a coordinator agent evaluates the data and drafts student reports.

Deploy AutoGen agent teams to debate grading policies, verify enrollments, and manage Moodle courses.

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Connect Moodle MCP to AutoGen

Create your Vinkius account to connect Moodle 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 Moodle with AutoGen

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Automate Course Creation Debates in AutoGen

Use `create_courses` to launch new classes after your AutoGen agents reach consensus on the curriculum. A curriculum planner agent proposes the course structure, while a compliance agent checks it against active categories using `list_categories`. Once both agents agree the shortname and category are valid, the execution agent calls the creation tool. This prevents administrative errors and ensures consistent naming conventions.

Coordinate Student Onboarding via AutoGen MCP Server

Run `create_users` to register new cohorts after your onboarding agents verify their enrollment eligibility. An admin agent checks the incoming roster, while a billing agent confirms payment status before triggering the account creation. Once verified, a third agent calls `enrol_users` to place them in the correct classes. This multi-agent coordination ensures no student is enrolled without passing all administrative gates.

Audit Grades through Multi-Agent Consensus

Retrieve `get_user_grades` to let your agent team audit academic performance across departments. A teacher agent flags failing scores, while an advisor agent suggests intervention steps based on assignments pulled via `list_assignments`. The agents debate the best course of action before drafting a notification. This collaborative approach ensures that grade evaluations are thorough and backed by actual LMS data.

Setup guide

Set up Moodle 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 Moodle 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="Moodle_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Moodle. 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 Moodle MCP in AutoGen

One agent checks categories using `list_categories`, while another drafts the course details. Once they agree, the execution agent calls `create_courses`.

Yes. An advisor agent can call `get_user_grades` to retrieve scores, while a coordinator agent evaluates the data and drafts student reports.

Use the AutoGen MCP tool adapter to convert the server's tools. Pass the resulting tool list directly to your AssistantAgent constructor.

Yes. Your agents can loop through a list of student IDs and call `enrol_users` sequentially to add them to a course.

Yes. Your Moodle administrative tokens and user passwords passed to `create_users` are encrypted in transit and never cached by the execution container. This keeps your MCP integration completely secure.

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