How to Use the eduMe MCP in CrewAI

Deploy specialized CrewAI agent teams to manage and audit your eduMe training programs via our MCP Server.

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Connect eduMe MCP to CrewAI

Create your Vinkius account to connect eduMe 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 eduMe with CrewAI

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Run collaborative training audits with CrewAI

Divide and conquer your training compliance. You can set up a researcher agent to scan teams using `list_training_teams` while an analyst agent evaluates the results. The analyst agent uses `quick_team_training_audit` to pinpoint underperforming groups. This multi-agent approach ensures you find training gaps without manual data crunching.

Onboard new trainees using this MCP Server

Let your agents handle the administrative work of managing user lists. A coordinator agent can run `search_trainees_by_keyword` to verify if a new hire is already registered. If the user is missing, the agent flags the account for creation. This keeps your team rosters accurate across your entire organization.

Curate custom learning paths automatically

Match the right courses to the right users. One agent can fetch a user's current progress using `get_user_training_profile` to see what they have completed. A second agent queries `list_top_performing_courses` to select the most effective next steps. They collaborate to build a tailored training recommendation list.

Setup guide

Set up eduMe 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 eduMe tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the endpoint URL directly into the server setup parameter when defining your CrewAI agents. The framework automatically discovers and configures all ten tools.

Yes, CrewAI's shared memory allows agents to pass tool outputs. For example, one agent can fetch details with `get_course_details` and share them with the rest of the crew.

Use the server class and define a tool filter during initialization. This lets you restrict access, ensuring agents can only call safe tools like `list_training_courses`.

Set up a dedicated monitor agent to poll your account metrics. The agent uses `get_edume_account_metadata` to keep track of active usage and limits.

All communications are encrypted end-to-end through Vinkius's MCP platform. Trainee names, email identifiers, and course completion records are never cached or exposed outside the active execution runtime.

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