How to Use the Zesty.io MCP in Mastra AI

Q: If I use Zesty.io in Mastra AI, what should I check for failures?

You can build logic around item existence. For example, if getcontentitem fails because the ID is wrong, your workflow can automatically retry or notify an admin.

Q: Does Zesty.io support multiple environments with Mastra AI?

Yes. Use listzestyinstances to identify and target multiple independent Zesty.io accounts within a single, comprehensive workflow run.

Build complex, failure-proof workflows for Mastra AI agents.

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Connect Zesty.io MCP to Mastra AI

Create your Vinkius account to connect Zesty.io 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 Zesty.io with Mastra AI

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Full Content Item Lifecycle

Your agent needs to process data robustly? Start by calling `create_content_item` within a workflow step. Then, the next step can use `get_content_item` to verify the newly created record's details. The final action in your Mastra AI flow might be an update; simply call `update_content_item` to modify fields on existing content records.

System Setup and Discovery

Before running a workflow, the agent must know what it’s dealing with. Use `list_content_models` to gather all available schemas for your Zesty.io environment. This is crucial setup data. Need to see which environments are attached? Calling `list_zesty_instances` provides a comprehensive list of accounts and instances associated with the workflow.

Clean Up Records Automatically

If a step fails, you might need to roll back or clean up. Use `delete_content_item` to remove specific content records when necessary. This tool is perfect for cleanup steps in an automatic retry mechanism. Don't forget about settings. Retrieving instance details via `get_instance_settings` allows the agent to check prerequisites before committing to a major action.

Setup guide

Set up Zesty.io 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 Zesty.io 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: "zestyio-mcp-client",
  servers: {
    "zestyio-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Zesty.io MCP in Mastra AI

The MCP Server provides structured data actions (create, read, update) that your workflow engine can call conditionally. It allows complex business logic to interact with content management.

The primary data type is structured content records defined by models, along with instance-level configuration settings and model schemas themselves.

You can build logic around item existence. For example, if `get_content_item` fails because the ID is wrong, your workflow can automatically retry or notify an admin.

Yes. Use `list_zesty_instances` to identify and target multiple independent Zesty.io accounts within a single, comprehensive workflow run.

The server touches content models, individual content items, and high-level instance settings. Your agent manipulates these structured records.

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