How to Use the 360Learning MCP in LlamaIndex

Q: Can I filter 360Learning data in LlamaIndex?

You can use the allowedtools filter to restrict which parts of the platform your agent can access. This ensures only relevant data reaches your index.

Index your training data from 360Learning directly into your LlamaIndex vector store for grounded answers.

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Connect 360Learning MCP to LlamaIndex

Create your Vinkius account to connect 360Learning 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 360Learning with LlamaIndex

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Index course data in LlamaIndex

Call `get_course` to pull lesson content and feed the results into your vector index. This makes your training documentation searchable alongside your other private documents.

Retrieve 360Learning stats with LlamaIndex

Use `get_stats` to fetch completion data and store it as indexed knowledge. You can then ask your agent about trends in student performance based on historical API snapshots.

Map 360Learning groups via MCP Server

Run `list_groups` to catalog your departments and cohorts within your knowledge base. Your agent uses these IDs to narrow down searches when you ask about specific team training progress.

Setup guide

Set up 360Learning 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 360Learning 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 360Learning tools.",
)
response = await agent.run("List recent 360Learning 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 360Learning. 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 360Learning MCP in LlamaIndex

Use the MCP tool spec to convert platform data into a format LlamaIndex understands. The results become part of your queryable knowledge store.

Yes, by combining live API output with your vector stores, your agent answers questions grounded in current platform data.

You can use the allowed_tools filter to restrict which parts of the platform your agent can access. This ensures only relevant data reaches your index.

Trigger the tools periodically to refresh your vector store. The agent pulls the latest stats or course lists to keep your knowledge up to date.

We only process the specific course and learner records you authorize. The data is stored locally within your vector index and never shared elsewhere.

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