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 LangChain 10 tools — connect in under 2 minutes

Name: Umbraco
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Umbraco through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "umbraco": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Umbraco, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Umbraco through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Umbraco MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Umbraco via MCP

Why Use LangChain with the Umbraco MCP Server

LangChain provides unique advantages when paired with Umbraco through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Umbraco MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Umbraco queries for multi-turn workflows

Umbraco + LangChain Use Cases

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

RAG with live data: combine Umbraco tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Umbraco, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Umbraco tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Umbraco tool call, measure latency, and optimize your agent's performance

Umbraco MCP Tools for LangChain (10)

These 10 tools become available when you connect Umbraco to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Umbraco to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Umbraco + LangChain FAQ

Common questions about integrating Umbraco MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.