Can I see the completion results for a specific student?

Yes! Use the `get_training_completion_results` tool. The agent will return completion data and grades for enrolments, allowing you to track individual or aggregate success.

How do I check which groups a member belongs to?

Use the `get_member_group_associations` tool with the member ID. Your agent will fetch all groups the specific member is part of, giving you context on their organizational alignment.

Where do I find my Coassemble User ID and Token?

Log in to Coassemble, navigate to **Settings**, and select **API**. You can generate and copy your User ID and API User Token from that section.

Coassemble MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Coassemble
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Coassemble through Vinkius, pass the Edge URL in the `mcps` parameter and every Coassemble 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Coassemble Specialist",
    goal="Help users interact with Coassemble effectively",
    backstory=(
        "You are an expert at leveraging Coassemble 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 Coassemble "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

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

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Coassemble MCP Server

Connect your Coassemble account to any AI agent and take full control of your online training and LMS through natural conversation. Streamline how you manage learners, courses, and completion results natively.

When paired with CrewAI, Coassemble becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Coassemble 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 details for all training courses in your workspace natively
Enrolment Intelligence — Access and monitor student enrolments and their current progress flawlessly
Member Management — List all workspace members and their contact details securely
Group Logistics — Monitor student groups and manage their course associations flawlessly
Completion Auditing — Retrieve training results and grades for all enrolments to track success flawlessly
Profile Visibility — Access your own user profile and core workspace metadata directly within your workspace

The Coassemble MCP Server exposes 8 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 Coassemble to CrewAI via MCP

Follow these steps to integrate the Coassemble 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 8 tools from Coassemble

Why Use CrewAI with the Coassemble MCP Server

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

Coassemble + CrewAI Use Cases

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

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

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

Coassemble MCP Tools for CrewAI (8)

These 8 tools become available when you connect Coassemble to CrewAI via MCP:

enroll_member_in_course

Enroll a specific member into a course or group

get_course_training_details

Get detailed information for a specific course

get_member_group_associations

Get all groups a specific member belongs to

get_training_completion_results

List training results and grades for enrolments

list_coassemble_courses

List all training courses in the Coassemble workspace

list_coassemble_enrolments

List all course enrolments

list_coassemble_groups

List all student groups in the workspace

list_coassemble_members

List all members (users) in the workspace

Example Prompts for Coassemble in CrewAI

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

"List all training courses in my Coassemble workspace."

"Show me the progress for user 'STU_12345'."

"What are the latest completion results?"

Troubleshooting Coassemble MCP Server with CrewAI

Common issues when connecting Coassemble 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.

Coassemble + CrewAI FAQ

Common questions about integrating Coassemble 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.