Can it easily retrieve the content of a page assuming I only know its URL path?

Absolutely. Instruct the agent to 'get_delivery_content_by_path' and supply something like '/home/about'. It traverses the Delivery API native routing map and returns perfectly all inner block grids and content variables attached.

How do I ensure the JSON payloads for `create_cms_document` are exactly what Umbraco expects?

Use the agent to first run 'list_document_types'. It pulls down the global schema blueprints. The LLM can explicitly read the allowed field types (like rich text or media pickers) and logically construct the insertion matching it exactly.

Does it pull media details dynamically?

Yes. Pinging 'list_media_assets' grants full insight over all stored visuals inside your Umbraco media containers, returning URL addresses alongside fundamental dimension formats so the agent can reference them anywhere.

Umbraco MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Umbraco
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Umbraco through the Vinkius — pass the Edge URL in the `mcps` parameter and every Umbraco 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="Umbraco Specialist",
    goal="Help users interact with Umbraco effectively",
    backstory=(
        "You are an expert at leveraging Umbraco 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 Umbraco "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 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 Umbraco MCP Server

Connect your Umbraco CMS backend to any AI agent and take full autonomous control bridging the powerful Delivery and Management APIs purely through natural conversation.

When paired with CrewAI, Umbraco becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Umbraco tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Delivery API Traversing — Instantly list public pages, query by content type, or securely fetch structured fields by their exact domain paths organically
Backoffice Document Control — Push new document permutations natively adhering to your configured schemas without opening a single GUI panel
Site Mutations — Command the targeted removal of any outdated published nodes or force updates to internal fields seamlessly via update_cms_document
Schema & Media Insight — Grab absolute lists tracking your stored binary media files alongside the global Document Types blueprints mapped out natively

The Umbraco MCP Server exposes 10 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 Umbraco to CrewAI via MCP

Follow these steps to integrate the Umbraco 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 10 tools from Umbraco

Why Use CrewAI with the Umbraco MCP Server

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

Umbraco + CrewAI Use Cases

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

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

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

Umbraco MCP Tools for CrewAI (10)

These 10 tools become available when you connect Umbraco to CrewAI via MCP:

create_cms_document

Provide the document data as a JSON object adhering to the schema. Creates a new document in the Umbraco CMS

delete_cms_document

This action is irreversible. Permanently deletes a document from the Umbraco CMS

get_delivery_content_by_id

Retrieves a specific content item by its GUID or numeric ID via Delivery API

get_delivery_content_by_path

g., "/home/about"). Retrieves a specific content item by its URL path

get_management_document

Retrieves a specific document via the Umbraco Management API (Drafts/Backoffice)

list_delivery_content

Supports pagination via take and skip. Lists content available via the Umbraco Delivery API

list_document_types

Lists all document types (schemas) defined in Umbraco

list_media_assets

Lists media assets (images, files) from the Umbraco Media library

query_delivery_content

g., "contentType:blogPost"). Filters content items using the Umbraco Delivery API query syntax

update_cms_document

Provide the document ID and JSON updates. Updates fields of an existing document in Umbraco

Example Prompts for Umbraco in CrewAI

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

"Use the delivery API to get the content from '/products/new-feature' and list out its properties."

"Look up our Document Types to see the exact schema required for a 'BlogPost'. Then create one JSON draft placeholder payload based on it."

"Delete the backoffice document holding ID d6ef43..."

Troubleshooting Umbraco MCP Server with CrewAI

Common issues when connecting Umbraco 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

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Umbraco + CrewAI FAQ

Common questions about integrating Umbraco 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.