How do I enable Web Services in Moodle?

Go to Site Administration > Server > Web services > Overview. You'll need to enable Web Services, enable the REST protocol, create a specific user, and generate a token for them.

Can I see student grades for a course?

Yes! Use the `get_course_grades` tool with a course ID to retrieve the gradebook data for all students enrolled in that course.

How do I check if a student has completed a course?

The `get_course_completion` tool allows you to check the progress and completion status for a specific User ID in a target Course ID.

Moodle MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Moodle
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 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.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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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 10 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 LMS account to your AI agent and streamline your educational and course management workflows 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

Course Management — List all courses available to your web service user and view high-level details.
User & Enrollment Oversight — Search for students or teachers and list all users enrolled in a specific course.
Activity Tracking — Access all quiz, assignment, and workshop activities within your courses.
Progress Monitoring — Check course completion status for individual users and retrieve detailed gradebook data.
Group Tracking — View all user groups defined within your courses.
Direct Messaging — Send instant messages to any Moodle user ID directly from your chat.

The Moodle MCP Server exposes 10 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.

How to Connect Moodle to CrewAI via MCP

Follow these steps to integrate the Moodle MCP Server with CrewAI.

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 10 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

Moodle MCP Tools for CrewAI (10)

These 10 tools become available when you connect Moodle to CrewAI via MCP:

get_course_assignments

List assignments in courses

get_course_completion

Get course completion status

get_course_grades

Get course grades

get_course_groups

List groups in a course

get_course_quizzes

List quizzes in courses

get_course_workshops

List workshops in courses

get_courses

List Moodle courses

get_enrolled_users

List users in a course

get_users

Search for Moodle users

send_message

Send an instant message

Example Prompts for Moodle in CrewAI

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

"List all my available Moodle courses."

"What are the grades for students in course ID 5?"

"Check if user 'John Doe' (ID: 123) has finished the 'Cybersecurity' course (ID: 8)."

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.