How to Use the Classe365 MCP in Mastra AI

Q: How do I add the Classe365 MCP Server to Mastra AI?

Initialize the MCPClient with your Vinkius URL. Call listTools() and spread the results directly into your agent's tool array.

Q: How do I filter Classe365 students in a Mastra AI workflow?

Pass the liststudents tool to your agent. You can set up conditional branching to process active enrollments differently than alumni.

Q: Does Mastra AI require human approval for Classe365 updates?

You control the permissions. Setting requireToolApproval on write operations pauses the workflow until an admin signs off.

Design resilient admissions workflows and automated grading pipelines in Mastra AI using live Classe365 data.

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Connect Classe365 MCP to Mastra AI

Create your Vinkius account to connect Classe365 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 Classe365 with Mastra AI

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Automate Admissions Workflows

Onboarding hundreds of pupils requires handling failures gracefully. You can build a Mastra AI workflow that triggers `create_student_profile` through this MCP Server when a web form is submitted. If the API times out, the built-in exponential backoff tries again. Once the profile exists, the next step in the pipeline automatically pulls their schedule using `list_school_classes`.

Audit Academic Records via MCP Server

Administrators need reliable reports on department performance. Your agent can loop through `list_academic_records` and aggregate the data across all sections. Bad data won't break the run. Mastra catches any errors when calling `list_exam_assessments` and routes the failure to a human-in-the-loop approval queue before continuing.

Sync Attendance Automatically

Tracking who showed up should happen without manual clicks. Set up a cron job that kicks off an agent to run `list_students` every afternoon. The workflow iterates through the roster and checks `list_student_attendance` for anomalies. You deploy this entire pipeline to your cloud provider with a single command.

Setup guide

Set up Classe365 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 Classe365 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: "classe365-mcp-client",
  servers: {
    "classe365-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Classe365 Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Classe365 tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Classe365 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 Classe365. 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 Classe365 MCP in Mastra AI

Initialize the `MCPClient` with your Vinkius URL. Call `listTools()` and spread the results directly into your agent's tool array.

Built-in exponential backoff handles temporary drops automatically. If a call to add a student fails, the workflow waits and tries again without writing custom loop logic.

Pass the `list_students` tool to your agent. You can set up conditional branching to process active enrollments differently than alumni.

You control the permissions. Setting `requireToolApproval` on write operations pauses the workflow until an admin signs off.

We isolate every execution in an ephemeral sandbox. Your attendance logs and academic assessments disappear from memory the millisecond the workflow completes.

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