How to Use the Image Router MCP in Mastra AI

Q: How does Image Router handle model failures inside Mastra AI workflows?

The MCP Server uses checkimagerouterstatus to verify API availability before your agent starts a run. If an endpoint is down, Mastra AI can catch the error and route the prompt to an alternative model.

Q: Can a Mastra AI agent query style options using Image Router?

Yes, the agent calls liststyles to fetch a list of supported visual styles. It can then inject these styles into the prompt to ensure the output matches your brand guidelines.

Q: How do I get the status of long-running generations in Mastra AI with Image Router?

You use getgenerationstatus inside a Mastra workflow loop to poll for completion. This allows your agent to pause execution and resume only when the image file is fully rendered.

Q: Does the Image Router support manual model selection?

Yes, while the routing is automatic, you can use listmodels to inspect available options and force your Mastra AI agent to use a specific model via the MCP Server.

Run multi-step image generation workflows with Mastra AI and this automated MCP Server.

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Conditional workflow routing with Mastra AI

The `generate_image_advanced` tool integrates into your Mastra agent workflows to handle complex, multi-step image creation tasks. This MCP Server lets the workflow engine automatically retry with a different model using the exact same parameters. By using `list_models_by_category`, your agent can dynamically switch between illustration and photorealism models based on the workflow's current state. This prevents your backend pipelines from stalling when a specific API is offline.

Automated asset editing and variation steps

The `edit_image` tool lets your Mastra agents modify generated assets as part of a larger, automated content pipeline. The agent takes the output of a previous step, applies editing instructions, and spits out the updated image. You can chain this with `generate_variation` to produce multiple design options for A/B testing. The workflow engine handles the branching logic while the server picks the best model for each variant.

Precision upscaling and model tracking

The `upscale_image` tool handles the final step of your Mastra workflow by scaling up selected drafts for production use. Your agent triggers this tool only after validating the initial output quality. To ensure you are using the right model, `get_model` retrieves detailed specifications for the selected engine. This lets your agent calculate costs and log performance metrics before executing expensive generation runs.

Setup guide

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

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

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

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Common questions about Image Router MCP in Mastra AI

The MCP Server uses `check_imagerouter_status` to verify API availability before your agent starts a run. If an endpoint is down, Mastra AI can catch the error and route the prompt to an alternative model.

Yes, the agent calls `list_styles` to fetch a list of supported visual styles. It can then inject these styles into the prompt to ensure the output matches your brand guidelines.

You use `get_generation_status` inside a Mastra workflow loop to poll for completion. This allows your agent to pause execution and resume only when the image file is fully rendered.

Yes, while the routing is automatic, you can use `list_models` to inspect available options and force your Mastra AI agent to use a specific model via the MCP Server.

Your prompt parameters, style configurations, and image coordinates are processed entirely in memory. The server does not persist your generation inputs, protecting your proprietary design prompts from exposure.

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