How to Use the Modelbit (ML Model Deployments) MCP in Mastra AI

Build complex ML-driven workflows in Mastra AI with the Modelbit MCP Server.

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Connect Modelbit (ML Model Deployments) MCP to Mastra AI

Create your Vinkius account to connect Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) with Mastra AI

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Perplexity

Automate inference in multi-step workflows

Drop `get_inference` into your Mastra agent definitions to build decision trees based on model outputs. If a model returns a specific probability, your agent can branch immediately to the next task. This is how you handle complex business logic without hard-coding every scenario. Your agent asks the model, gets the data, and keeps the process moving.

Retry failed inferences automatically

Mastra AI handles the heavy lifting when connections drop. By using the Modelbit MCP Server, you get automatic retries and exponential backoff for every inference call. Your workflow stays robust even if the network blips. You don't need to write custom error-handling loops because the framework manages the state for you.

Enforce human-in-the-loop for model calls

Sometimes you need a person to check the model output before the workflow proceeds. Use the tool approval feature in Mastra to pause after `get_inference` executes. Your team can verify the result in the dashboard and approve the next step. It's the perfect balance between speed and control for sensitive operations.

Setup guide

Set up Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) 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: "modelbit-ml-model-deployments-mcp-client",
  servers: {
    "modelbit-ml-model-deployments-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Modelbit (ML Model Deployments) transactions"
);
console.log(result.text);

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Common questions about Modelbit (ML Model Deployments) MCP in Mastra AI

Import the MCP client into your Mastra agent configuration. Map the `get_inference` tool to your agent's tools array, and it becomes available for all your workflows.

It does. Because you're using the Mastra framework, you can configure backoff policies directly on the tool call to ensure your ML pipeline survives transient failures.

You can. The output from your inference call acts as a variable in your workflow, allowing your agents to make decisions based on real-time model data.

Data is transmitted over an encrypted channel. The server acts as a thin bridge, ensuring your payload reaches the Modelbit endpoint without local storage or logging.

The server returns a standard error message from the API. Your Mastra agent catches this, allowing you to trigger a fallback step or notify an admin.

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