How to Use the Udemy MCP in Mastra AI

Q: How do I handle Udemy review retrieval using Mastra AI?

You use instructorreviews in a multi-step workflow. If the initial call fails, you can build logic to wait and retry automatically with exponential backoff until the reviews are successfully pulled.

Q: Does Mastra AI support complex Udemy data pipelines?

Yes. You can chain tools like courses (to get metadata) into a subsequent action that uses instructormessages. The framework handles dependencies and failure states for you.

Q: How do I list all available courses for an instructor using Mastra AI?

You call instructorcourses within a dedicated workflow step. If that step encounters an error—say, rate limiting—the framework will automatically pause and retry the request.

Build reliable Udemy data workflows with Mastra AI.

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Create your Vinkius account to connect Udemy 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 Udemy with Mastra AI

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Automate Instructor Feedback Analysis

Use `instructor_reviews` to pull all feedback for an instructor's entire catalog. Mastra AI handles this complex analysis by allowing you to build a workflow that retries if the initial review call fails. You can set up conditional logic: If the reviews are too sparse, trigger a secondary step to fetch messages via `instructor_messages` before notifying the admin.

Manage Udemy Course Discovery Pipelines

Need to check multiple courses? The `courses` tool lets you get details on specific Udemy offerings. Mastra AI's workflow engine executes this, ensuring that if one ID fails, it automatically retries the entire process using a different batch of IDs. This is crucial for building reliable pipelines where data retrieval can fail due to temporary API limits.

Gather Comprehensive Instructor Data

You can pull all instructor QA questions using `instructor_qa` and then combine them with the list of courses taught via `instructor_courses`. Mastra AI manages this multi-step, complex data gathering process. The workflow ensures that if listing the courses fails, the whole job pauses gracefully, preventing bad data from reaching the next step.

Setup guide

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

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

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

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Common questions about Udemy MCP in Mastra AI

You use `instructor_reviews` in a multi-step workflow. If the initial call fails, you can build logic to wait and retry automatically with exponential backoff until the reviews are successfully pulled.

Yes. You can chain tools like `courses` (to get metadata) into a subsequent action that uses `instructor_messages`. The framework handles dependencies and failure states for you.

You can build an autonomous process. For example, check the instructor's QA questions (`instructor_qa`), then list their courses (`instructor_courses`). This entire operation runs reliably even if a step fails.

The server touches course IDs, review text, and message content. You build the workflow around these strings, giving you full control over which data segments are processed.

You call `instructor_courses` within a dedicated workflow step. If that step encounters an error—say, rate limiting—the framework will automatically pause and retry the request.

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