Bring E Learning
to CrewAI
Learn how to connect Moodle to CrewAI and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the Moodle MCP Server?
Connect your Moodle instance to any AI agent and manage your learning platform through natural conversation.
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
How it works
1. Subscribe to this server
2. Enter your Moodle Site URL and API Token
3. Start managing courses from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Educators — automate course creation and student enrolment
- Administrators — bulk-manage users and grade retrieval
- EdTech — integrate LMS data into AI-powered workflows
Built-in capabilities (12)
Requires fullname, shortname, and categoryid. Create new courses
Requires username, password, firstname, lastname, and email. Create new users in Moodle
Enrol users into a course
Get contents of a specific course
Get users enrolled in a course
Get Moodle site information
Get grades for a user in a course
List assignments for courses
List course categories
List all available courses
List files in a specific area
g., username, email) and value. Search for users in Moodle
Why CrewAI?
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.
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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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
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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 in CrewAI
Moodle and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Moodle to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Moodle in CrewAI
The Moodle 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. All 12 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Moodle for CrewAI
Every tool call from CrewAI to the Moodle MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
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.
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.
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.