How to Use the Webiny CMS MCP in CrewAI

Q: How does CrewAI handle drafts and content creation in Webiny CMS?

The team uses createcmsentry to generate new draft records. The specialized 'Researcher' agent can then use getapiintrospection to ensure the required JSON fields are correctly identified before writing.

Q: Can I schedule a multi-step publishing sequence in Webiny CMS with CrewAI?

Yes. You define an 'Editor' agent whose role is to call publishcmsentry. The entire crew waits until the Editor signals success before marking the content as live.

Q: What happens if a model entry needs fixing, and how does CrewAI manage it?

A 'Reviewer' agent can call getmodelentrydetails to pull the problematic data. The team then passes this specific revision ID to another agent for targeted correction using updatecmsentry.

Run autonomous multi-agent teams that manage and publish Webiny CMS content with CrewAI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Webiny CMS MCP to CrewAI

Create your Vinkius account to connect Webiny 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.

GDPR Free for Subscribers

Setup Webiny CMS with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Coordinate Content Creation Across Teams

The team can automate the entire draft process. Agent A calls `list_model_entries` to find existing drafts, while Agent B uses `get_model_entry_details` to research missing fields. The shared memory allows a Moderator Agent to watch both of these actions and decide if more data needs to be gathered before proceeding.

Build Autonomous Publishing Pipelines for Webiny CMS

You can design a pipeline where an agent drafts the content using `create_cms_entry`, and then a second, specialized agent monitors that draft. The final action is executed by calling `publish_cms_entry` only when all criteria are met. The CrewAI framework manages this sequential execution with high reliability.

Handle Complex Content Updates via MCP Server

If content needs refinement, one agent can call `update_cms_entry` to modify specific fields. A second agent can then execute a validation query using `execute_graphql_query`. The roles ensure that the update is not permanent until both agents confirm the data passes all required business logic checks.

Setup guide

Set up Webiny 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 Webiny CMS tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Webiny 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 →

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

    task = Task(
        description="List recent Webiny 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 Webiny. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Webiny CMS now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Webiny CMS MCP in CrewAI

The team uses `create_cms_entry` to generate new draft records. The specialized 'Researcher' agent can then use `get_api_introspection` to ensure the required JSON fields are correctly identified before writing.

Yes. You define an 'Editor' agent whose role is to call `publish_cms_entry`. The entire crew waits until the Editor signals success before marking the content as live.

A 'Reviewer' agent can call `get_model_entry_details` to pull the problematic data. The team then passes this specific revision ID to another agent for targeted correction using `update_cms_entry`.

The server provides a list of model names and their current status via `list_model_entries`. This foundational data allows your CrewAI team to scope its operations effectively.

Absolutely. You can assign different agents roles for different model types (e.g., one for 'Articles' and another for 'Case Studies'). Each agent uses `list_model_entries` to manage its specific domain.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript