How to Use the Moodle MCP in CrewAI

Q: How do I filter Moodle tools for specific CrewAI agents?

Use the toolfilter in your agent definition. This ensures your researcher agent only sees getcourses while your moderator agent only has access to sendmessage.

Deploy autonomous Moodle crews with CrewAI for collaborative academic monitoring and administrative action.

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Connect Moodle MCP to CrewAI

Create your Vinkius account to connect Moodle to CrewAI 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 CrewAI

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Collaborative Course Monitoring Crews

Assign agents to monitor `get_course_assignments` and `get_course_quizzes` simultaneously. One agent identifies gaps, and another prepares the required data. Your crew shares memory, so the entire team knows the status of a course. This approach removes the need for human oversight during routine academic checks.

Autonomous Administrative Action

Use `send_message` as an action tool for your moderator agent. Once the research agent identifies an issue via `get_course_completion`, the moderator sends the notification. This pipeline runs without human intervention. You define the roles, and the agents coordinate their actions to keep your Moodle instance updated.

Specialized User Management Agents

Task specific agents with `get_users` and `get_enrolled_users` to maintain accurate rosters. These agents work in the background to ensure your data stays current. By splitting these tasks, your crew operates more efficiently. Each agent focuses on a specific toolset, reducing the complexity of your overall operation.

Setup guide

Set up Moodle MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Moodle tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Moodle Analyst",
    goal="Access and analyze Moodle data via MCP.",
    backstory="Expert analyst with direct Moodle access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Moodle transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Moodle Analyst",
        goal="Access and analyze Moodle data via MCP.",
        backstory="Expert analyst with direct Moodle access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Moodle transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 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 CrewAI

Yes, agents share memory in CrewAI. One agent can fetch data using `get_course_grades` and pass that context to another agent for analysis.

Use the `tool_filter` in your agent definition. This ensures your researcher agent only sees `get_courses` while your moderator agent only has access to `send_message`.

It is. Because you can deploy crews of specialized agents, you can distribute the load of checking thousands of students across multiple concurrent processes.

Data is scoped to the agent session. Your Moodle credentials are never shared between agents, and information is discarded once the task is completed.

Yes, you can set up a manager agent to oversee the Moodle tools. The manager delegates tasks to sub-agents, ensuring a structured approach to your data.

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