How to Use the EdApp MCP in Mastra AI

Q: What's the process for connecting the EdApp MCP to a Mastra AI agent?

Install the @mastra/mcp package, then instantiate a new MCPClient in your code using your private Vinkius server URL. After that, you can pass the EdApp tools into your agent definition to start building.

Q: Can I add a human approval step to my EdApp workflows?

You can. Mastra AI supports a requireToolApproval option. This lets you build a workflow that identifies inactive users but waits for a manager to approve the removelearner action before it runs.

Automate EdApp user onboarding and reporting. Build resilient training workflows that never fail with Mastra AI.

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Connect EdApp MCP to Mastra AI

Create your Vinkius account to connect EdApp 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 EdApp with Mastra AI

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Automated Learner Sync

Build a workflow that keeps your EdApp user base perfectly in sync with your HR system. Your Mastra AI agent can run on a schedule, call `list_learners` from EdApp, and compare the list against your company's source of truth. When the workflow detects a new employee, it automatically calls `create_new_learner`. When someone leaves, it triggers `remove_learner`. Mastra's built-in retry logic ensures these critical API calls always go through, even if EdApp's API is temporarily unavailable.

Build Unbreakable Reporting Pipelines

You can stop running manual training reports. A Mastra AI workflow can kick off every Monday morning, call `get_catalog_statistics` for a high-level overview, then get details with `list_training_courses`. From there, it can loop through each course and call `get_course_progress_report` for all active users. If any API call fails, Mastra retries it automatically. You can even add a step that sends the final report to a specific Slack channel, creating a completely hands-off process.

Intelligent Onboarding via this MCP Server

Go beyond just creating users. With Mastra AI, you can build an intelligent onboarding sequence. After `create_new_learner` adds a user, the workflow can check their department data from another system. Based on that data, the agent can assign them to the right training by using `list_course_collections`. You can even program the workflow to check `get_lesson_attempts_log` after 48 hours and send a reminder if the new hire hasn't started their training.

Setup guide

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

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

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

Mastra AI has automatic retries with exponential backoff built right in. If a call to an EdApp tool like `get_course_progress_report` fails, it will try again, which makes your automation scripts much more reliable.

Yes, this is a perfect use case for Mastra AI. You can create a workflow that periodically calls `list_learners`, checks progress with `get_learner_details`, and then invokes `remove_learner` for any accounts that meet your inactivity criteria.

Install the `@mastra/mcp` package, then instantiate a new `MCPClient` in your code using your private Vinkius server URL. After that, you can pass the EdApp tools into your agent definition to start building.

You can. Mastra AI supports a `requireToolApproval` option. This lets you build a workflow that identifies inactive users but waits for a manager to approve the `remove_learner` action before it runs.

All tool calls, such as those fetching learner progress or account info, are isolated within Vinkius's zero-trust infrastructure. Your Mastra AI agent only communicates via a secure, token-authenticated endpoint, and the server itself has no persistent storage for your EdApp data.

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