How to Use the Innform MCP in Mastra AI

Build resilient training workflows in Mastra AI with automatic retries and conditional branching for your LMS data.

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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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Automated workflow branching

Use `list_learners` and `list_departments` to trigger different paths in your Mastra agent. If a user is missing a required course, the agent can automatically flag them for follow-up. This keeps your compliance processes moving without manual intervention. You define the logic, and the agent executes it based on the live data retrieved.

Fail-safe data retrieval

Configure your agent to retry calls to `list_results` if the network blips. Mastra handles the backoff logic so your background processes don't stall. Your training syncs stay consistent even if the network is unstable. It's built to handle complex operations where reliability is the main requirement.

Structured learner management

Update your internal records by pulling data through `get_learner` and mapping it to your local database. Your agent acts as the bridge between your systems. This keeps your data accurate across platforms. You get a clear view of who has completed what without ever leaving your agent environment.

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-mcp-client",
  servers: {
    "innform-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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

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

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

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

You can force a human-in-the-loop check before any tool call executes. This ensures an admin reviews the data before your agent takes action.

Yes, you can iterate through the tool list to process large datasets. Just keep an eye on your API rate limits during high-volume operations.

Store your credentials in environment variables and inject them into the client. The server handles the authentication handshake automatically.

The workflow engine detects the failure and pauses. You can configure it to retry later or send an alert to your Slack channel.

The connection uses a dedicated, encrypted tunnel for every request. Only the specific learner IDs you query are ever accessed by the agent.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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