How to Use the Maestra MCP in Mastra AI

Orchestrate complex media localization workflows in Mastra AI with built-in retries and conditional branching.

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Connect Maestra MCP to Mastra AI

Create your Vinkius account to connect Maestra 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 Maestra with Mastra AI

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Resilient media workflows in Mastra AI

Define agent chains that use `upload_media_for_transcription` as the first step. If the upload fails, your Mastra workflow catches the error and retries automatically. This adds stability to your media pipeline. You don't have to write custom logic for every potential network hiccup.

Conditional voice generation

Use `list_available_ai_voices` to decide which voice model to use based on the target language. Your agent logic switches models dynamically before calling `generate_ai_voiceover`. It ensures your localized content maintains a consistent brand voice. You get full control over the audio output quality.

Automated file management

Monitor your account storage using `list_maestra_files` and `list_account_folders` within your agent loops. The tools tell you exactly when files are ready for export. This keeps your workflow state clean. You only trigger the next step once the file status confirms the data is ready.

Setup guide

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

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

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

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Common questions about Maestra MCP in Mastra AI

Yes. You can import the Maestra toolset into your Mastra agents to automate transcription and translation tasks within your existing logic.

Use the requireToolApproval setting. It forces the agent to pause before calling tools like `translate_transcription` until you sign off.

Absolutely. You configure the retry policies in your agent definition to handle transient API errors without stopping the whole workflow.

The results feed back into the agent memory. You can then pass that data to the next step in your sequence.

All interactions are encrypted in transit. The server acts as a bridge, ensuring your credentials and media paths stay isolated from other processes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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