How to Use the Webiny CMS MCP in LlamaIndex

Q: How do I manage publishing status with Webiny CMS and LlamaIndex?

You use listmodelentries for current status checks. For the actual change, you process the results through a system that calls publishcmsentry and indexes the resulting 'read' state.

Q: Does Webiny CMS provide locale support with LlamaIndex?

Yes. When running GraphQL queries via executegraphqlquery, you must specify the locale, ensuring that your knowledge base is aware of multi-language content revisions.

Build RAG applications with Webiny CMS and LlamaIndex.

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Connect Webiny CMS MCP to LlamaIndex

Create your Vinkius account to connect Webiny CMS 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 Webiny CMS with LlamaIndex

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Search Past Content Revisions

The `get_model_entry_details` tool lets you retrieve deep details for a specific content model entry, referencing the revision ID. You can index these detailed outputs into your vector store. This means your RAG application doesn't just answer questions; it answers them using actual historical API data from Webiny CMS.

Index Global Settings

Use `get_tenant_config` to pull the global settings for the entire Webiny tenant. Indexing this configuration allows your knowledge base to answer questions like, 'What is the default publishing setting?' This capability grounds your search results in system-level truth.

Track Draft Statuses

The `list_model_entries` tool lets you pull a list of entries. By indexing both 'manage' and 'read' outputs, LlamaIndex can track whether content is currently draft status or live. This capability helps the agent answer complex questions like, 'Show me all articles that are ready to publish but haven't been.'

Setup guide

Set up Webiny CMS 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 Webiny CMS 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 Webiny CMS tools.",
)
response = await agent.run("List recent Webiny CMS 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 Webiny. 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 Webiny CMS MCP in LlamaIndex

LlamaIndex indexes the raw API data from Webiny CMS. Instead of just calling a tool, it turns the output into searchable chunks, so your answers are grounded in actual content revisions.

Yes. By listing entries for different model plural names (e.g., 'Articles' and 'Products') and indexing the results, your knowledge base can answer cross-model questions.

The server touches content model entries, which include text fields, revision IDs, tenant configuration settings, and API metadata. All these structured outputs can be added to your index.

You use `list_model_entries` for current status checks. For the actual change, you process the results through a system that calls `publish_cms_entry` and indexes the resulting 'read' state.

Yes. When running GraphQL queries via `execute_graphql_query`, you must specify the locale, ensuring that your knowledge base is aware of multi-language content revisions.

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