How to Use the Magnolia (Enterprise Headless CMS) MCP in CrewAI

Q: Can a crew of agents work together on a single piece of content?

Yes, that's the point of CrewAI. One agent can use mg.getdeliverynode to fetch content, a second can analyze it, and a third can use mg.patchcmsnode to update it, all within the same process, sharing context through the crew's shared memory.

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Connect Magnolia (Enterprise Headless CMS) MCP to CrewAI

Create your Vinkius account to connect Magnolia (Enterprise Headless CMS) 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 Magnolia (Enterprise Headless CMS) with CrewAI

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Assemble a Content Auditing Crew

Stop auditing your CMS by hand. You can deploy a CrewAI team where each agent has a specific job. For example, a 'Scout Agent' uses `mg.list_jcr_workspaces` and `mg.query_delivery_nodes` to find all content modified in the last month. It passes this list to a 'Compliance Agent', which uses `mg.get_template_schema` to verify that each piece of content conforms to the latest schema rules. A 'Reporter Agent' then compiles the findings and sends a summary. The entire audit runs without you lifting a finger.

Build an Autonomous Moderation Team

Keep your content clean automatically. A 'Monitor Agent' can run on a schedule, using `mg.query_delivery_nodes` to look for newly created content. When it finds a new node, it tasks a 'Reviewer Agent' to fetch its content with `mg.get_delivery_node`. If the content contains banned keywords or fails a check, the Reviewer alerts an 'Enforcer Agent'. This agent can then use `mg.patch_cms_node` to either unpublish the content or flag it for human review. This is how you manage content at scale.

Deploy a Content Lifecycle MCP Server Crew

Manage your content's entire lifecycle with a specialized crew. An 'Archivist Agent' can identify old or stale content. Before deletion, it passes the node to a 'Validator Agent', which uses `mg.get_delivery_children` to ensure the node has no active child pages that are still in use. Only when the Validator gives the all-clear does a 'Janitor Agent' get tasked with using `mg.wipe_cms_node` for permanent deletion. This role-based separation prevents accidental data loss and makes your cleanup process much safer.

Setup guide

Set up Magnolia (Enterprise Headless CMS) 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 Magnolia (Enterprise Headless CMS) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Magnolia (Enterprise Headless CMS) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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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="Magnolia (Enterprise Headless CMS) Analyst",
        goal="Access and analyze Magnolia (Enterprise Headless CMS) data via MCP.",
        backstory="Expert analyst with direct Magnolia (Enterprise Headless CMS) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Magnolia (Enterprise Headless CMS) 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 Magnolia CMS. 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 Magnolia (Enterprise Headless CMS) MCP in CrewAI

When you define your agent in CrewAI, you pass it a list of tool objects. You can use the `tool_filter` in the MCP server configuration to expose only certain tools, like giving `mg.wipe_cms_node` to a 'Janitor Agent' but not to a 'Scout Agent'.

Yes, that's the point of CrewAI. One agent can use `mg.get_delivery_node` to fetch content, a second can analyze it, and a third can use `mg.patch_cms_node` to update it, all within the same process, sharing context through the crew's shared memory.

Build a broken link checker crew. One agent crawls your site, a second uses `mg.query_delivery_nodes` to find the corresponding nodes in Magnolia, and a third agent flags them for review or attempts to fix them. It's a simple, high-value automation.

The MCP tools appear to the agent just like any other Python function. Whether your crew runs sequentially or hierarchically, an agent can call a tool like `mg.execute_workspace_command` at its designated step, and the result is returned to the agent to inform its next action.

The MCP server ensures data is handled on a per-call basis. If one agent calls `mg.get_template_schema`, only the schema definition is processed. This data can be passed between agents in memory, but each tool execution is an atomic, sandboxed event. Your Magnolia credentials are never part of the process.

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