How do I get an eduMe API Key?

Log in to your eduMe dashboard, navigate to **Settings > API**, and you can generate or retrieve your unique API Key from there. API access may require an enterprise-level plan.

Can the agent assign courses to users?

This integration currently focuses on listing and auditing training data. Assigning courses or creating new users should be managed via the eduMe dashboard or specific enrollment API endpoints.

Does the integration show real-time completion?

Yes, you can use the list_training_courses or get_user_training_profile tools to retrieve the latest completion rates and statuses as reported by the eduMe platform.

eduMe MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: eduMe
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to eduMe through Vinkius, pass the Edge URL in the `mcps` parameter and every eduMe tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="eduMe Specialist",
    goal="Help users interact with eduMe effectively",
    backstory=(
        "You are an expert at leveraging eduMe tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in eduMe "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About eduMe MCP Server

Integrate eduMe, the leading mobile-first training platform for the deskless workforce, directly into your AI workflow. Manage your training courses and modules, track trainee profiles and completion rates, monitor team performance, and oversee your organizational learning metadata using natural language.

When paired with CrewAI, eduMe becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call eduMe tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Course Oversight — List and retrieve detailed information and completion metrics for all your mobile training courses.
Trainee Intelligence — Monitor user training profiles, identifying completed courses, active enrollments, and organizational team memberships.
Team Management — Access and monitor all training teams and user groups configured in your eduMe account.
Learning Auditing — Retrieve high-level summaries of team activity, course engagement, and organizational training health.

The eduMe MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect eduMe to CrewAI via MCP

Follow these steps to integrate the eduMe MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from eduMe

Why Use CrewAI with the eduMe MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with eduMe through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

eduMe + CrewAI Use Cases

Practical scenarios where CrewAI combined with the eduMe MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries eduMe for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries eduMe, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain eduMe tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries eduMe against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

eduMe MCP Tools for CrewAI (10)

These 10 tools become available when you connect eduMe to CrewAI via MCP:

get_course_details

Get detailed settings and module list for a specific training course

get_edume_account_metadata

Retrieve metadata and limits for your eduMe account

get_user_training_profile

Get full training history and profile for a specific user

list_latest_training_content

Identify the most recently created or updated training courses

list_top_performing_courses

Identify courses with the highest completion or engagement rates (mock logic)

list_trained_users

List all users registered in your eduMe training platform

list_training_courses

List all mobile training courses available in eduMe

list_training_teams

List all teams and user groups configured in your eduMe account

quick_team_training_audit

Retrieve a high-level summary of team activity and member counts

search_trainees_by_keyword

Search for users using a name keyword or external identifier

Example Prompts for eduMe in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with eduMe immediately.

"List all mobile training courses."

"Show me the training profile for user 'john_doe'."

"Which teams have the lowest course engagement?"

Troubleshooting eduMe MCP Server with CrewAI

Common issues when connecting eduMe to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

eduMe + CrewAI FAQ

Common questions about integrating eduMe MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.