How to Use the LearnUpon MCP in LlamaIndex

Q: Does LlamaIndex support filtering LearnUpon tools?

You can use the allowedtools filter to restrict which API calls your agent can make. This is useful for keeping your agent focused on specific tasks.

Index your LearnUpon training data with LlamaIndex. Ground your RAG applications in live course catalogs and learner records.

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Connect LearnUpon MCP to LlamaIndex

Create your Vinkius account to connect LearnUpon 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 LearnUpon with LlamaIndex

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Semantic Search for LearnUpon

Turn your course catalog into a searchable knowledge base using `list_courses`. LlamaIndex ingests these results to provide grounded answers about your training inventory. You no longer hunt through web portals. Your agent retrieves the exact course details from the API and indexes them for instant recall.

Index Learner Progress

Retrieve `list_enrollments` output and store it in your vector database. This allows your agent to answer questions about specific learner completion rates by querying the index. It turns static API data into a dynamic resource. You'll get precise answers about who finished what without manually checking each record.

Update LearnUpon Records via Agents

Trigger `update_user` through your agent to modify account status based on your indexed data. When your analysis shows a need for change, the agent makes the call directly. This closes the loop between data discovery and system action. You get a system that learns from its own findings.

Setup guide

Set up LearnUpon 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 LearnUpon 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 LearnUpon tools.",
)
response = await agent.run("List recent LearnUpon data")

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Common questions about LearnUpon MCP in LlamaIndex

Use the MCP tool spec to pull data from the server. Once retrieved, convert that tool output into nodes for your vector index.

You can use the allowed_tools filter to restrict which API calls your agent can make. This is useful for keeping your agent focused on specific tasks.

Absolutely. You can mix API output with local text files to give your agent a complete view of your training ecosystem.

Only if you configure your index to do so. Otherwise, the agent fetches live data from the server on every request.

The server operates within a strict sandbox. It only interacts with the specific LearnUpon user IDs and email strings you authorize, ensuring your internal data never leaks.

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