Can I create courses and enrol students?

Yes. Create courses with full metadata, then enrol users with specific role IDs (student, teacher, manager).

How does Moodle authentication work?

Moodle requires your **Site URL** and an **API Token** (wstoken). The token is passed as a query parameter to the Web Service REST endpoint.

Can I retrieve student grades?

Yes. Retrieve grade items and results for any user within a specific course using their course and user IDs.

INSTANT PLUG AND PLAY

Moodle MCP Server for CrewAIGive CrewAI instant access to 12 tools to Create Courses, Create Users, Enrol Users, and more

Name: Moodle
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Moodle through Vinkius, pass the Edge URL in the `mcps` parameter and every Moodle tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

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The Moodle app connector for CrewAI is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Moodle Specialist",
    goal="Help users interact with Moodle effectively",
    backstory=(
        "You are an expert at leveraging Moodle tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Moodle "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

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

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Moodle MCP Server

Connect your Moodle instance to any AI agent and manage your learning platform through natural conversation.

When paired with CrewAI, Moodle becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Moodle tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Courses — List, create, and browse course content sections
Users — Search, create, and manage learner profiles
Enrolment — Enrol users into courses and view enrolled learners
Grades — Retrieve grade items and student results per course
Assignments — List assignments for specific courses
Categories — Browse course organization categories
Files — Access files attached to course modules
Site Info — Retrieve Moodle instance metadata

The Moodle MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 12 Moodle tools available for CrewAI

When CrewAI connects to Moodle through Vinkius, your AI agent gets direct access to every tool listed below — spanning e-learning, course-management, student-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_courses

Requires fullname, shortname, and categoryid. Create new courses

create_users

Requires username, password, firstname, lastname, and email. Create new users in Moodle

enrol_users

Enrol users into a course

get_course_contents

Get contents of a specific course

get_enrolled_users

Get users enrolled in a course

get_site_info

Get Moodle site information

get_user_grades

Get grades for a user in a course

list_assignments

List assignments for courses

list_categories

List course categories

list_courses

List all available courses

list_files

List files in a specific area

list_users

g., username, email) and value. Search for users in Moodle

Connect Moodle to CrewAI via MCP

Follow these steps to wire Moodle into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Moodle

Why Use CrewAI with the Moodle MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Moodle through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Moodle + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Moodle MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Moodle for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Moodle, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Moodle tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Moodle against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Moodle in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Moodle immediately.

"List all courses and show enrolment counts."

"Get grades for student ID 42 in the Python course."

"Create a new course 'AI Ethics' in category 1 and enrol 3 students."

Troubleshooting Moodle MCP Server with CrewAI

Common issues when connecting Moodle to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Moodle + CrewAI FAQ

Common questions about integrating Moodle MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.