How to Use the Viesus MCP in Mastra AI

Q: How do I manage Viesus jobs using Mastra AI?

You initiate the job with enhanceimageai and then your workflow waits, periodically calling getenhancementjobstatus. Mastra AI handles this polling loop automatically, ensuring you get the final result when it's ready.

Q: Can Mastra AI handle multiple failure points with Viesus?

Absolutely. You can build conditional logic: if checkapihealth fails, then retry the whole workflow after a cool-down period; that's exactly what this framework is for.

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Connect Viesus MCP to Mastra AI

Create your Vinkius account to connect Viesus 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 Viesus with Mastra AI

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Handle Job Failures Automatically

You're running a multi-step workflow—maybe the upload fails or the status check times out. Mastra AI manages this with automatic retries and exponential backoff. You just call `enhance_image_ai`, and the system handles the messy failure logic for you. This means your complex workflows don't break on a temporary network hiccup; they automatically try again until they succeed.

Manage Complex Pre-Processing Steps

Before calling `enhance_image_ai`, you might need to gather context. Your workflow can first call `list_enhancement_presets` to get available styles, then use the result to build a final configuration object. This sequential execution capability means your agent can decide *what* parameters to pass based on intermediate data.

Complete Workflow Auditing

A major workflow involves multiple actions over time. You don't want the final step to fail because of an unlogged initial action. Your agent can first list all `list_processing_history` records, then use that data to decide if a retry is needed. It’s crucial for compliance and debugging complex operations.

Setup guide

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

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

const result = await agent.generate(
  "List recent Viesus 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 Viesus. 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 Viesus MCP in Mastra AI

You initiate the job with `enhance_image_ai` and then your workflow waits, periodically calling `get_enhancement_job_status`. Mastra AI handles this polling loop automatically, ensuring you get the final result when it's ready.

This server primarily deals with image processing metadata and job status. When running a workflow, you pass initial inputs like file identifiers, and the output includes the final download URL from `get_enhanced_image_url`.

Absolutely. You can build conditional logic: if `check_api_health` fails, then retry the whole workflow after a cool-down period; that's exactly what this framework is for.

You do when you have complex business logic. If your operation requires multiple steps, failure handling, or retries across different services, this pairing is powerful.

The server touches image processing metadata and job status records. You are primarily dealing with the file identifiers and the technical details of the images being processed.

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