How to Use the Innform MCP in Mastra AI

Q: How is my Innform group and progress data protected?

Every request to listgroups or listcompletedassignments runs inside a zero-trust, sandboxed environment. Your LMS credentials are encrypted at rest and never exposed to the agent framework.

Build resilient, self-healing LMS workflows using Mastra AI and the Innform MCP Server.

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Connect Innform MCP to Mastra AI

Create your Vinkius account to connect Innform 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 Innform with Mastra AI

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Resilient LMS workflows with Mastra AI

Stop letting network hiccups break your training pipeline. This MCP Server integrates with Mastra AI to run conditional workflows that automatically retry actions like `invite_user` if the initial request fails. If a new learner invitation fails, Mastra's built-in engine retries with exponential backoff. You don't have to write custom error-handling code to keep your onboarding pipeline moving.

Conditional compliance enforcement

Automate how you handle overdue training without human intervention. Your agent can run scheduled checks using `list_overdue_assignments` and execute a multi-step logic branch based on what it finds. If an employee is past due, the workflow can notify their manager first, wait a day, and then run `freeze_user` if they still haven't completed the course.

Automated group reassignments

Keep your training cohorts organized. Use `list_groups` to scan your current team structures, then dynamically update learner profiles using `update_user` as they finish modules. If a user completes their compliance path, the workflow automatically moves them to the next group, keeping your team's training records clean.

Setup guide

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

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

const result = await agent.generate(
  "List recent Innform 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 Innform. 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.

Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

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Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Innform MCP in Mastra AI

Instantiate the client with your server URL, call the tool listing method, and spread them into your agent's tools array. This gives your agent immediate access to operations like `get_user_details`.

Yes. You can configure Mastra's approval flags for sensitive operations like `freeze_user`. The workflow will pause and wait for an administrator's sign-off before locking the account.

Mastra features a built-in workflow engine that catches API errors during `update_user` operations. It will automatically re-attempt the update using exponential backoff, preventing broken state syncs.

You can deploy your Mastra agent to any cloud provider with a single command. The agent runs as a stateless service that connects to your LMS via secure MCP protocols.

Every request to `list_groups` or `list_completed_assignments` runs inside a zero-trust, sandboxed environment. Your LMS credentials are encrypted at rest and never exposed to the agent framework.

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