How to Use the 360Learning MCP in CrewAI

Q: How do I restrict certain 360Learning actions in CrewAI?

Use the MCPServerHTTP class and apply a toolfilter. You can allow the reporting agent to read stats while blocking it from creating users.

Deploy specialized CrewAI agents to manage 360Learning MCP Server training programs autonomously.

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Connect 360Learning MCP to CrewAI

Create your Vinkius account to connect 360Learning to CrewAI 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 CrewAI

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Run Autonomous 360Learning MCP Crews

Managing a corporate university takes a crew of agents analyzing user stats. Instead of doing it yourself, you assign specific roles to different AI profiles. One agent monitors compliance while another handles actual course assignments. The researcher agent runs `list_users` to build a target list of active learners. It passes that memory context to an analyst agent that calls `get_stats`. They work together sequentially to find exactly who needs intervention.

Delegate Departmental Reporting

Executives want weekly updates on training completion rates across different groups. You can build a hierarchical crew where a manager agent oversees the data collection. It delegates the grunt work to specialized subordinates. A reporting agent executes `list_groups` to map out the organizational structure. It then iterates through those IDs using `get_stats` to compile assessment scores. The manager agent reviews the final dataset before formatting it into a summary email.

Audit Course Catalogs Automatically

Keeping track of outdated training materials requires scanning course details continuously. Let a dedicated auditor agent scan your library in the background. The user sets the parameters once and walks away. This auditor triggers `list_courses` to pull the active inventory. Next, it uses `get_course` to inspect the technical details and lesson overviews of each module. If it spots missing metadata, it flags the course for human review.

Setup guide

Set up 360Learning MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke 360Learning tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="360Learning Analyst",
    goal="Access and analyze 360Learning data via MCP.",
    backstory="Expert analyst with direct 360Learning access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent 360Learning transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="360Learning Analyst",
        goal="Access and analyze 360Learning data via MCP.",
        backstory="Expert analyst with direct 360Learning access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent 360Learning transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

Add the endpoint URL to the mcps array when defining your Agent. Run pip install crewai[tools] to ensure the correct dependencies are present.

Yes. The framework uses shared memory to pass context between agents. One profile can pull a course ID and hand it off to another for detailed inspection.

Use the MCPServerHTTP class and apply a tool_filter. You can allow the reporting agent to read stats while blocking it from creating users.

Vinkius manages the OAuth flow and token rotation on the backend. Your Python script just needs the single endpoint URL to start executing commands.

The Vinkius architecture guarantees zero persistent storage for your training data. Completion rates stream directly into your local Python environment through an encrypted transport layer. The server sandbox terminates the moment the crew finishes its task.

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