How do I find my API Key ID and Secret?

Log in to your LearnUpon portal, navigate to Settings > API, and you will be able to generate and copy your API Key ID and Secret.

Can I search for a user by email?

Yes, use the `search_users` tool and provide the email address to retrieve the specific learner profile.

Is it possible to unenroll a user via the agent?

Absolutely. Use the `unenroll_user` tool with the unique enrollment ID to remove a user from a course.

LearnUpon MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: LearnUpon
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="LearnUpon Specialist",
    goal="Help users interact with LearnUpon effectively",
    backstory=(
        "You are an expert at leveraging LearnUpon 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 LearnUpon "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

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

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 LearnUpon MCP Server

Connect your LearnUpon portal to any AI agent to automate your learning management operations. This MCP server enables your agent to interact with learner accounts, course catalogs, and enrollment data directly.

When paired with CrewAI, LearnUpon becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call LearnUpon 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

Learner Management — List and search for users, and automate the creation or updating of learner profiles
Course Discovery — Query your entire course library and search for specific training content by name
Enrollment Automation — Manage user enrollments, link learners to courses, and handle unenrolling when needed
Progress Tracking — Monitor enrollment statuses and identify learner participation across your portal
Bulk Operations Support — Retrieve paginated lists of data to maintain large-scale learning environments

The LearnUpon MCP Server exposes 9 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 LearnUpon to CrewAI via MCP

Follow these steps to integrate the LearnUpon 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 9 tools from LearnUpon

Why Use CrewAI with the LearnUpon MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with LearnUpon 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

LearnUpon + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries LearnUpon 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 LearnUpon, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain LearnUpon 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 LearnUpon against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

LearnUpon MCP Tools for CrewAI (9)

These 9 tools become available when you connect LearnUpon to CrewAI via MCP:

create_user

Requires email/username, password, and name. Create a new learner account

enroll_user_in_course

Requires course_id and user identification. Enroll a user into a specific course

list_courses

List all available courses

list_enrollments

List all course enrollments

list_users

Use this to identify user IDs for enrollment or updates. List all learner accounts

search_courses

Search for courses by name

search_users

Search for users by email or username

unenroll_user

Remove a user enrollment from a course

update_user

Update an existing user account

Example Prompts for LearnUpon in CrewAI

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

"Find the user with email 'learner@example.com' in LearnUpon."

"Search for courses related to 'Cybersecurity'."

"Enroll user ID '12345' into course ID '101'."

Troubleshooting LearnUpon MCP Server with CrewAI

Common issues when connecting LearnUpon 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.

LearnUpon + CrewAI FAQ

Common questions about integrating LearnUpon 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.