How to Use the Umbraco MCP in LlamaIndex

Q: How can I search Umbraco content using Umbraco and LlamaIndex?

You use the querydeliverycontent tool to pull structured data. LlamaIndex then takes that result set and embeds it into a vector store, letting you ask complex, natural language questions against the live content.

Q: Does Umbraco support indexing drafts with Umbraco and LlamaIndex?

Yes. You can use getmanagementdocument to fetch draft records. Indexing these means your knowledge base includes unpublished, but relevant, content for deep searching.

Q: If I use Umbraco to create documents, how does LlamaIndex handle the changes?

You can run createcmsdocument and then immediately feed that new document's content into the indexing pipeline. This keeps your knowledge base current with the latest official records.

Index Umbraco data and query it with LlamaIndex.

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Connect Umbraco MCP to LlamaIndex

Create your Vinkius account to connect Umbraco to LlamaIndex 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 LlamaIndex

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Querying Content via MCP Server

Need to find specific articles without knowing the URL? You call `query_delivery_content` by criteria, like `contentType:blogPost`. The output is then indexed into your vector store. This means you can later query past API data using semantic search. Instead of just listing results, LlamaIndex treats that content list as searchable knowledge. It answers questions based on the actual Umbraco data returned.

Building a Knowledge Base with LlamaIndex

The `list_document_types` tool provides a schema map of your entire site, and that list gets indexed. You can then ask questions like, 'What types of content do we support?' and get an answer grounded in the Umbraco API data, not just generic knowledge. This turns metadata discovery into deep, verifiable information retrieval.

Indexing Media Assets with LlamaIndex

You use `list_media_assets` to get a list of files. By indexing this data, you can build a searchable knowledge base around your media library—knowing file types, descriptions, and availability without running the API call again. This is massive for RAG applications; you're combining live Umbraco asset details with textual documents in one unified index.

Setup guide

Set up Umbraco MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Umbraco MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Umbraco tools.",
)
response = await agent.run("List recent Umbraco data")

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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 LlamaIndex

You use the `query_delivery_content` tool to pull structured data. LlamaIndex then takes that result set and embeds it into a vector store, letting you ask complex, natural language questions against the live content.

Yes. You can use `get_management_document` to fetch draft records. Indexing these means your knowledge base includes unpublished, but relevant, content for deep searching.

The server touches document content (text/JSON), schema definitions (`list_document_types`), and media asset metadata. All these discrete data streams become part of your searchable knowledge index.

You can run `create_cms_document` and then immediately feed that new document's content into the indexing pipeline. This keeps your knowledge base current with the latest official records.

A direct query gives you a snapshot of data at that moment. Using LlamaIndex grounds your answers by indexing that data first, allowing you to answer follow-up questions or perform comparative searches later.

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