How to Use the Moodle MCP in LlamaIndex

Q: How does LlamaIndex index Moodle course data?

The framework calls getcourses and getcourseassignments to fetch raw text data. It then parses these responses into document nodes and inserts them into your vector store.

Q: Can I search Moodle student groups using LlamaIndex?

Yes, you can use getcoursegroups within an index pipeline to organize your vector data by student groups. This helps your agent answer queries specific to particular student cohorts.

Q: Does the LlamaIndex integration support live grade lookups?

Yes, the agent can bypass the static index and call getcoursegrades directly for real-time queries. This ensures you get current grade calculations rather than cached vector data.

Q: Is my Moodle user profile data safe when using LlamaIndex?

All user profiles retrieved via getusers are encrypted in transit and run within isolated V8 containers. We do not persist any student names or identifiers on our servers.

Build RAG pipelines that index Moodle courses and student metrics directly into your LlamaIndex vector store.

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Connect Moodle MCP to LlamaIndex

Create your Vinkius account to connect Moodle to LlamaIndex 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 LlamaIndex

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Index Moodle courses into LlamaIndex

`get_courses` retrieves the full list of active classes to populate your vector database. The tool extracts course metadata and titles, which your LlamaIndex pipeline indexes for semantic search. This lets users ask natural language questions about available classes and get answers grounded in real-time catalog data. Your agent queries the resulting index instead of making repetitive API calls to the server.

Query student profiles with this MCP Server

This MCP Server provides `get_users` and `get_enrolled_users` to query user identities and class rosters. These tools fetch specific profile fields and enrollment statuses directly from your database. LlamaIndex converts these user records into searchable documents within your index. You can then query student cohorts based on behavioral metrics without writing SQL queries.

Index Moodle grades inside LlamaIndex

`get_course_grades` extracts numerical student marks to feed your knowledge base. The tool pulls grades directly from the course database, providing the raw data needed for performance tracking. LlamaIndex structures this grading data alongside syllabus documents to give your queries context. When your agent answers questions about class performance, it uses actual grades rather than guessing.

Setup guide

Set up Moodle MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Moodle MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Moodle tools.",
)
response = await agent.run("List recent Moodle data")

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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 LlamaIndex

The framework calls `get_courses` and `get_course_assignments` to fetch raw text data. It then parses these responses into document nodes and inserts them into your vector store.

Yes, you can use `get_course_groups` within an index pipeline to organize your vector data by student groups. This helps your agent answer queries specific to particular student cohorts.

You install the required tool package, initialize the basic client pointing to your Vinkius endpoint, and convert the tools to a list. This gives your agent immediate access to endpoints like `get_course_quizzes` and `get_course_workshops`.

Yes, the agent can bypass the static index and call `get_course_grades` directly for real-time queries. This ensures you get current grade calculations rather than cached vector data.

All user profiles retrieved via `get_users` are encrypted in transit and run within isolated V8 containers. We do not persist any student names or identifiers on our servers.

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