How to Use the Webiny CMS MCP in Mastra AI

Q: What is the best way to list all content models in Webiny CMS using Mastra AI?

Simply use listmodelentries and specify the plural model name. The workflow engine reads these lists, ensuring you can process every draft entry programmatically within a single job.

Q: Does Mastra AI support previewing content before publishing to Webiny CMS?

Yes. You use executegraphqlquery and set the apitype to 'preview.' This allows your workflow to validate exactly how a piece of content looks live without actually changing its status.

Q: Can I pull global settings from Webiny CMS using the Mastra AI workflow?

You call gettenantconfig. The resulting tenant configuration data is passed as an artifact within your workflow, allowing subsequent steps to check global rules before writing any content.

Build resilient, multi-step workflows that manage Webiny CMS data with Mastra AI.

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Connect Webiny CMS MCP to Mastra AI

Create your Vinkius account to connect Webiny CMS to Mastra AI 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 Mastra AI

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Manage Complex Content Workflows for Webiny CMS

Mastra AI handles the entire content lifecycle. You can write a workflow that first calls `list_model_entries` to find drafts, then uses `get_model_entry_details` to validate required fields. The workflow engine manages this sequence and automatically retries if the initial API call fails due to temporary network issues.

Reliably Publish or Update CMS Content

Need to publish a draft? Set up a conditional step that runs `publish_cms_entry`. If the publication succeeds, the workflow moves on. If it fails (e.g., due to missing meta data), Mastra AI automatically triggers an admin notification. Similarly, updating content via `update_cms_entry` can be wrapped in a retry loop for maximum reliability.

Execute Controlled GraphQL Operations

You control the API interaction flow. Use `execute_graphql_query` to run both read and write operations, specifying the necessary api_type (manage/read). Mastra AI ensures that if a mutation fails—say, trying to `create_cms_entry` with bad JSON—the entire workflow gracefully rolls back instead of crashing.

Setup guide

Set up Webiny CMS MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Webiny CMS tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "webiny-cms-mcp-client",
  servers: {
    "webiny-cms-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Webiny CMS Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Webiny CMS tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Webiny CMS transactions"
);
console.log(result.text);

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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 Mastra AI

Mastra AI's built-in retry logic handles transient API failures. When you use `update_cms_entry`, the framework will automatically attempt to resend the request with exponential backoff until it succeeds or hits a hard failure limit.

Simply use `list_model_entries` and specify the plural model name. The workflow engine reads these lists, ensuring you can process every draft entry programmatically within a single job.

Yes. You use `execute_graphql_query` and set the api_type to 'preview.' This allows your workflow to validate exactly how a piece of content looks live without actually changing its status.

The server provides explicit error codes and messages detailing why `create_cms_entry` failed. Mastra AI captures this failure state and can use it to trigger a defined recovery action.

You call `get_tenant_config`. The resulting tenant configuration data is passed as an artifact within your workflow, allowing subsequent steps to check global rules before writing any content.

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