Moodle MCP Server

Overview

The Moodle MCP Server is a lightweight middleware that bridges language‑model assistants with the Moodle learning management system. It exposes a set of high‑level tools that let an AI client retrieve contextual information—such as assignment due dates, unread messages, pending quizzes, and enrolled courses—and present it in natural language. By handling authentication, API calls, and response formatting behind the scenes, the server frees developers from plumbing details and lets them focus on building richer conversational experiences.

At its core, the server runs a small Python web service that implements the Model Context Protocol. When an AI assistant sends a request, the server parses it, calls Moodle’s REST API endpoints, and returns structured data that the assistant can embed in its reply. The design supports both cloud‑hosted LLMs (e.g., OpenAI) and local models through Ollama, giving teams flexibility in how they deploy the assistant. The server’s modular architecture—separate client scripts for each LLM type, a configuration file for credentials, and a dedicated tool runner—makes it straightforward to extend or replace components without touching the core logic.

Key capabilities include:

• Assignment Insights: Fetches due dates and status for all assignments across courses, enabling the assistant to remind students of upcoming deadlines.
• Communication Tracking: Retrieves unread messages from Moodle’s messaging system, allowing the assistant to surface important announcements or instructor feedback.
• Quiz Monitoring: Lists quizzes that have not yet been completed, helping learners stay on top of assessment requirements.
• Course Catalog: Provides a full list of courses the user is enrolled in, which can be used for navigation or personalized content recommendations.

Typical use cases span both student and educator workflows. Students can ask the assistant, “What’s due tomorrow?” or “Show me my unread messages,” and receive instant, context‑aware answers. Educators can integrate the server into a teaching assistant bot that tracks student progress, highlights overdue work, or aggregates class participation metrics. Because the server speaks MCP, it can be plugged into any AI platform that supports the protocol—Claude, ChatGPT, or custom agents—without needing bespoke adapters.

What sets this MCP server apart is its focused domain coverage and dual‑model support. By concentrating on Moodle’s core features, it offers a highly tuned experience that reduces latency and simplifies error handling. The ability to switch between OpenAI and Ollama models without code changes means teams can experiment with cost‑effective local inference while still benefiting from the rich data provided by Moodle. This combination of domain expertise, protocol compliance, and flexible deployment makes the Moodle MCP Server a practical choice for developers looking to embed educational insights into conversational AI.