How to Use the AcademyOcean MCP in CrewAI

Q: How do I filter tools for AcademyOcean in CrewAI?

Use the toolfilter parameter in your configuration to limit which tools each agent can access. This keeps your crew focused and secure.

Deploy a specialized crew in CrewAI to manage AcademyOcean training operations.

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Connect AcademyOcean MCP to CrewAI

Create your Vinkius account to connect AcademyOcean 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 AcademyOcean with CrewAI

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Delegate AcademyOcean tasks to agents

Assign `list_learners` to your research agent while your moderator agent handles `invite_learner`. This splits the work between specialized roles for faster execution. You define the hierarchy and the shared memory. The crew handles the coordination, ensuring the right agent always has access to the right data.

Monitor training progress at scale

Use a monitor agent to poll `get_learner_progress` continuously. If the agent detects a bottleneck, it triggers a notification or escalates the issue to a human supervisor. This removes the need for manual oversight. Your crew operates autonomously, keeping your training programs on track 24/7.

Automate the course catalog

Let your research agent call `list_courses` to build a mapping of training requirements. It can then cross-reference this with your existing team structure to assign specific modules. This creates a self-managing training environment. You set the goals, and the agents execute the necessary steps to meet them.

Setup guide

Set up AcademyOcean 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 AcademyOcean tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent AcademyOcean 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="AcademyOcean Analyst",
        goal="Access and analyze AcademyOcean data via MCP.",
        backstory="Expert analyst with direct AcademyOcean access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent AcademyOcean 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 AcademyOcean. 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 AcademyOcean MCP in CrewAI

Yes, you connect the server directly to your agents. Each member of your crew can use the tools to perform their specific role in the training process.

It fits perfectly into hierarchical structures. You can grant specific tools to manager agents while keeping execution tools restricted to worker agents.

Use the tool_filter parameter in your configuration to limit which tools each agent can access. This keeps your crew focused and secure.

The server returns clear error messages to your agents. You can then program your crew to retry the action or escalate to a human.

Your training data is gated by your unique endpoint token. We ensure that only authorized agents can access learner emails and progress records, keeping everything isolated.

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