How to Use the 360Learning MCP in LangChain

Chain 360Learning API calls directly into your LangChain pipelines to automate training management without manual input.

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…and any MCP-compatible client

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Connect 360Learning MCP to LangChain

Create your Vinkius account to connect 360Learning to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate user onboarding in LangChain

Use `create_user` to trigger new account setups automatically when a new hire joins your Slack or email list. Pass the output directly into your next chain link to notify managers or assign specific training paths.

Build 360Learning data chains

Connect `get_stats` to your LangSmith traces to monitor how specific cohorts perform on assessments in real-time. Your agent can take these completion rates and immediately adjust the training schedule for underperforming groups.

Query 360Learning catalog via MCP Server

Search through your entire course library using `list_courses` to find the right material for any skill gap. This MCP Server lets your agent pull lesson overviews and technical details into your prompt context instantly.

Setup guide

Set up 360Learning MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes 360Learning tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "360learning-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent 360Learning transactions"
    })
    print(result["messages"][-1].content)

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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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect 360Learning now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about 360Learning MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You then fetch the tool definitions and inject them into your agent's tool set.

Yes, you can chain these operations together. The agent uses the output of one tool as the input for the next, letting it handle complex workflows.

LangSmith logs every tool execution. You get full visibility into how long each API call takes and what data returned from the platform.

It is stateless by default. Use the client session features if you need to keep context across multiple reasoning steps.

Your agent only touches the specific learner profiles you request. We enforce strict isolation so your training data never leaks between sessions.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.