How to Use the Moodle MCP in LlamaIndex

Q: How do I build a RAG pipeline using Moodle and LlamaIndex?

Load course materials using getcoursecontents, then index them into a VectorStoreIndex. Your LlamaIndex agent can then query this index directly.

Q: Can LlamaIndex index file attachments from my Moodle courses?

Yes. Use listfiles to locate course documents, download them, and ingest them into your LlamaIndex knowledge base.

Q: How do I search for specific students?

Use listusers to query the directory. You can index the results to enable semantic search on student profiles inside your application.

Index your Moodle courses directly into LlamaIndex to build RAG pipelines grounded in real classroom data.

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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 Course Contents into LlamaIndex Vector Stores

Use `get_course_contents` to extract syllabus data and feed it directly into your LlamaIndex document store. This tool pulls lecture notes and topics, converting raw LMS structures into searchable vector indexes. Your RAG pipeline queries this index to answer student questions. Instead of guessing, the agent references actual course files loaded via `list_files` to verify its answers.

Build Searchable Student Records with this MCP Server

Call `list_users` to retrieve student profiles and index them for quick directory searches. This lets you query your student roster semantically inside LlamaIndex without manual SQL lookups using this MCP integration. You can cross-reference these profiles with enrollment statuses by running `get_enrolled_users`. The resulting index helps your agent identify active students in real time.

Ground Grading Answers in Live LMS Data

Query `get_user_grades` to pull performance metrics and ground your academic advisor agent's feedback. By indexing these grades, LlamaIndex ensures that any study recommendations are based on actual performance. The agent matches these scores against upcoming tasks found via `list_assignments`. This creates a dynamic feedback loop where recommendations update as soon as new grades are posted.

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

Load course materials using `get_course_contents`, then index them into a VectorStoreIndex. Your LlamaIndex agent can then query this index directly.

Yes. Use `list_files` to locate course documents, download them, and ingest them into your LlamaIndex knowledge base.

Yes. You can use the async tool list methods in LlamaIndex to run `list_courses` and `list_categories` concurrently without blocking execution.

Use `list_users` to query the directory. You can index the results to enable semantic search on student profiles inside your application.

We isolate the MCP server execution environment. Your course files and assignment metadata retrieved via `list_files` are processed in memory and never stored on Vinkius servers.

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