How to Use the Umbraco MCP in LangChain

Q: How do I search for content using Umbraco and LangChain?

You use the querydeliverycontent tool. This lets you filter items, like finding all 'blogPost' types. The agent takes that filtered list and uses it as input for subsequent steps in your chain.

Q: Can LangChain help me manage Umbraco drafts?

Yep. You use getmanagementdocument to pull specific documents from the backoffice, even if they aren't published yet. This means your agent can work with draft content and build workflows around it.

Q: Does the MCP Server support bulk updates for Umbraco? With LangChain?

It does. You use updatecmsdocument to modify existing documents by providing a document ID and a JSON object of field changes. Your agent can loop through multiple IDs to batch-update content.

Q: If I delete something, how do I confirm it with Umbraco and LangChain?

The deletecmsdocument tool permanently deletes records. Because this is irreversible, your agent must be designed to include a confirmation or review step before calling this action.

Build multi-step Umbraco workflows with LangChain.

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Connect Umbraco MCP to LangChain

Create your Vinkius account to connect Umbraco to LangChain 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 Umbraco with LangChain

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Sequential Content Retrieval via MCP Server

Need to know what's at a specific URL? You call `get_delivery_content_by_path` and get back the full content payload. Your agent then takes that JSON, maybe passes it through another step (like summarizing it), before finally outputting the result. This makes your reasoning chains powerful. The outcome of retrieving content isn't just data; it's a verifiable input for the next tool call in the pipeline.

Automating Content Creation with LangChain

Launching a new article? You use `create_cms_document` to build the record and pass all necessary field data as a JSON object. Your agent doesn't just write the content; it manages the entire lifecycle, from schema adherence to final document creation. It’s perfect for multi-step reasoning where the agent first queries schemas (`list_document_types`), then drafts the payload, and finally executes the write command.

Managing Media Assets through MCP Server

Sometimes you need to list what's available in Umbraco’s media library. You call `list_media_assets` to get a manifest of all images and files. This output is then used by your agent to decide which asset ID should be placed into the newly created CMS document. This step-by-step process ensures that complex tasks—like creating an article *with* specific media—are executed reliably, one tool call at a time.

Setup guide

Set up Umbraco MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Umbraco tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "umbraco-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Umbraco transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Umbraco. 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 Umbraco MCP in LangChain

You use the `query_delivery_content` tool. This lets you filter items, like finding all 'blogPost' types. The agent takes that filtered list and uses it as input for subsequent steps in your chain.

Yep. You use `get_management_document` to pull specific documents from the backoffice, even if they aren't published yet. This means your agent can work with draft content and build workflows around it.

The server handles several types, including structured document payloads (JSON), media assets (file references), and full delivery content records. Your agent sees all these distinct inputs.

It does. You use `update_cms_document` to modify existing documents by providing a document ID and a JSON object of field changes. Your agent can loop through multiple IDs to batch-update content.

The `delete_cms_document` tool permanently deletes records. Because this is irreversible, your agent must be designed to include a confirmation or review step before calling this action.

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