How to Use the Joomag MCP in CrewAI

Q: How does a CrewAI agent get details about a specific Joomag issue?

Once an agent has an issue ID (likely from the listissues tool), it can use the getissue tool. This provides the specific metadata for that single edition, which is useful for deep-dive analysis.

Deploy autonomous agent crews to monitor, audit, and manage your Joomag publications around the clock with CrewAI.

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Connect Joomag MCP to CrewAI

Create your Vinkius account to connect Joomag 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 Joomag with CrewAI

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Assign a Crew to Monitor Performance

Set up a 'Monitoring Agent' whose only job is to periodically call `list_analytics`. If it detects a significant drop in views on a publication, it passes the publication ID to an 'Analyst Agent' via shared memory. The Analyst Agent then takes over. It uses `get_publication` and `list_issues` to dig into the content's recent history and configuration, trying to pinpoint a cause. This is how you build an autonomous team to watch your metrics.

Build a Content Curation Team

Delegate content organization to a dedicated crew. A 'Scout Agent' can use `list_publications` and `list_collections` to create a complete map of all your existing Joomag content. It passes this map to a 'Curator Agent'. The Curator's role is to analyze the map, identify thematic gaps, or suggest new collections based on publication metadata. Your agents work together to manage the library.

Automate Audience and Campaign Audits

Deploy a 'CRM Agent' to use `list_contacts` and `list_subscriptions`. Its task is to maintain an up-to-date picture of your audience. It can then pass this data to a 'Campaign Agent'. The Campaign Agent cross-references the audience list with data from the `list_campaigns` tool. This allows the crew to determine which campaigns are effectively reaching which contacts, all without your direct involvement. This MCP server gives your agents the senses they need.

Setup guide

Set up Joomag 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 Joomag tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

It's straightforward. You can pass your Vinkius MCP URL directly into the agent's `mcps` parameter during setup. CrewAI will automatically discover and equip the agent with all the Joomag tools from this server.

Yes, that's the ideal use case. For example, have one agent use `list_publications` to find publications, and a second agent use `list_issues` to check the status of each one. They can collaborate to perform a full content audit.

Once an agent has an issue ID (likely from the `list_issues` tool), it can use the `get_issue` tool. This provides the specific metadata for that single edition, which is useful for deep-dive analysis.

Yes. When setting up your MCP connection in CrewAI, you can use a `tool_filter`. This lets you create specialized agents, like one that can only read analytics with `list_analytics` and another that can only manage contacts with `list_contacts`.

Your agents will access Joomag data like contact details, publication settings, and analytics reports. The MCP Server acts as a secure gateway for every tool call. Each operation is a discrete, sandboxed transaction, which prevents data leakage between unrelated agent tasks even when they share memory.

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