Can I publish a draft content entry via chat?

Yes. The `publish_cms_entry` tool allows you to take any draft revision live. You just need to provide the entry ID and the model name, and your AI agent will handle the publication through the Webiny 'manage' API.

Is it possible to see the entire GraphQL schema of my instance?

Absolutely. Using the `get_api_introspection` tool, your agent can retrieve the full GraphQL schema introspection, giving you a complete overview of all available types, fields, and content models in your environment.

How do I add a new entry to a content model through conversation?

Use the `create_cms_entry` tool. Provide the singular model name (e.g., 'Article'), the target locale, and a JSON object containing the field data. Your agent will create the new draft entry instantly.

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

Name: Webiny CMS
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Webiny CMS Specialist",
    goal="Help users interact with Webiny CMS effectively",
    backstory=(
        "You are an expert at leveraging Webiny CMS 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 Webiny CMS "
        "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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* 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 Webiny CMS MCP Server

Connect your Webiny CMS instance to any AI agent and manage your headless content infrastructure through natural conversation.

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

Content Lifecycle — Create, update, publish, and delete content entries for any model directly from your agent
Model Discovery — List all entries for specific content models and browse available data structures using introspection
Advanced GraphQL — Execute raw GraphQL queries or mutations for custom logic and complex nested data operations
Revision Control — Retrieve specific entry details by ID to inspect metadata and field-level property values
API Management — Discover available types, fields, and models in your current environment through automated introspection
Global Config — Verify high-level tenant settings and configurations to ensure your CMS environment is healthy
Multi-Locale Support — Seamlessly manage content across different language locales (e.g., en-US, pt-BR)

The Webiny CMS 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 Webiny CMS to CrewAI via MCP

Follow these steps to integrate the Webiny CMS 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 Webiny CMS

Why Use CrewAI with the Webiny CMS MCP Server

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

Webiny CMS + CrewAI Use Cases

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

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

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

Webiny CMS MCP Tools for CrewAI (9)

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

create_cms_entry

Provide the singular model name and field data as a JSON object. Creates a new draft entry for a content model

delete_cms_entry

This action is irreversible. Permanently deletes a content entry revision

execute_graphql_query

Specify api_type (manage, read, preview) and locale. Executes a raw GraphQL query or mutation against the Webiny CMS API

get_api_introspection

Retrieves the GraphQL schema introspection for the Webiny instance

get_model_entry_details

ID refers to the specific revision. Retrieves details for a specific content model entry

get_tenant_config

Retrieves global settings for the Webiny tenant

list_model_entries

Provide the model plural name (e.g. "Articles"). Specify api_type (manage for drafts, read for live). Lists all entries for a specific content model in Webiny

publish_cms_entry

Provide the specific revision ID. Publishes a draft entry, making it available via the "read" API

update_cms_entry

Provide the entry ID and a JSON object containing the field updates. Updates fields of an existing content entry revision

Example Prompts for Webiny CMS in CrewAI

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

"List all entries for the 'BlogPosts' model in en-US."

"Create a new 'Author' entry: { 'name': 'John Doe', 'bio': 'Tech Writer' } in en-US."

"Publish the entry with ID 'post-123' for model 'Article'."

Troubleshooting Webiny CMS MCP Server with CrewAI

Common issues when connecting Webiny CMS 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.

Webiny CMS + CrewAI FAQ

Common questions about integrating Webiny CMS 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.