How to Use the Workload MCP in Mastra AI

Q: How do I manage Workload status with Mastra AI?

Use checkworkloadstatus to verify connectivity. Because you're building highly complex, multi-step workflows with the Mastra AI framework, knowing the immediate operational state is critical for reliability.

Q: What tools are needed to define a new Workload in Mastra AI?

You start by calling createworkflow. This tool lets your agent build out the necessary structure. Once defined, you use other tools like getworkflow to confirm all parameters are set correctly before deploying.

Q: Can I see logs for a specific Workload run with Mastra AI?

Yes. You call listlogs, which lets your agent retrieve the historical records of an execution. This is essential when debugging conditional branching or failure points in your complex pipelines.

Q: How do I verify existing Workload automations using Mastra AI?

Use listworkflows. This provides a complete manifest of every defined workflow in your system, letting you audit the entire automation setup before deployment.

Build failure-proof, conditional workflows that guarantee complex operations with Mastra AI.

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Controlling Workflow State via the MCP Server

You need to ensure a complex job only runs when conditions are met. Use `enable_workflow` and `disable_workflow` to manage state. Your agent can check the current status using `get_workflow` before making changes. This control is crucial for building reliable systems that require human review or specific timing windows, letting you wrap the core functionality in checks like `check_workload_status`.

Managing and Rerunning Failed Jobs with Mastra AI

When a job fails deep within your multi-step process, don't let it stall. Call `retry_execution`. The Mastra AI framework handles the complex retries automatically, but you need to trigger them with this tool. Additionally, if you want to see what happened before the failure, use `list_logs` or narrow down the scope using `list_executions_by_workflow`.

Mapping Out Complex Operational Flows

Start by listing everything: `list_connections`, `list_workflows`, and `list_executions`. This gives your agent a full picture of the environment. The toolset allows you to build structured plans using tools like `create_workflow`. If you need to know what connection details are available, simply use `get_connection`. It keeps all components of your complex workflow documented in one place.

Setup guide

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

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

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

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Common questions about Workload MCP in Mastra AI

Use `check_workload_status` to verify connectivity. Because you're building highly complex, multi-step workflows with the Mastra AI framework, knowing the immediate operational state is critical for reliability.

You start by calling `create_workflow`. This tool lets your agent build out the necessary structure. Once defined, you use other tools like `get_workflow` to confirm all parameters are set correctly before deploying.

Yes. You call `list_logs`, which lets your agent retrieve the historical records of an execution. This is essential when debugging conditional branching or failure points in your complex pipelines.

This server handles workflow definitions, connection credentials, and operational logs. It manages the core state and history of all automated business processes.

Use `list_workflows`. This provides a complete manifest of every defined workflow in your system, letting you audit the entire automation setup before deployment.

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