How to Use the Vast.ai (GPU Rental Cloud API) MCP in Mastra AI

Q: How does Mastra AI handle GPU searching with Vast.ai (GPU Rental Cloud API)?

Your agent calls searchoffers to query the marketplace using JSON criteria. If that search fails, Mastra AI automatically retries the operation with exponential backoff built into its workflow engine.

Q: Can Mastra AI check existing GPU rentals?

Yes. It uses listinstances to pull all active resource data. This allows your conditional branching logic to determine if an action is necessary before proceeding with the rest of the workflow.

Q: What happens when I rent resources using Vast.ai (GPU Rental Cloud API) in Mastra AI?

The agent calls rentinstance. If the deployment fails for any reason, Mastra AI's core engine automatically retries the process until it succeeds or hits a defined limit.

Q: Does Mastra AI support manual intervention?

It does. You can set up requireToolApproval so that if the workflow hits a critical state, it pauses and waits for a human to approve the next step before continuing.

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Connect Vast.ai (GPU Rental Cloud API) MCP to Mastra AI

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Setup Vast.ai (GPU Rental Cloud API) with Mastra AI

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Search for required GPU resources

When a workflow starts, your agent first needs to know what's available. It uses `search_offers` to query the entire Vast.ai marketplace using specific criteria like {"gpu_name":{"eq":"RTX 4090"}}. This step grounds your complex multi-step process in real-world availability. If that initial search fails, Mastra AI's built-in exponential backoff logic automatically retries the search with a slight delay before notifying the admin. The workflow keeps moving even when things go sideways.

Monitor current GPU fleet status

Before taking action, your agent calls `list_instances` to get a full report of currently rented resources. This ensures that the next step in the workflow—whether it's scaling up or tearing down—is based on accurate data. The framework handles the state management for this check, allowing you to conditionalize the rest of your steps: if the instance count is over X, then proceed; otherwise, notify a different endpoint.

Execute provisioning and cleanup actions

To start a new resource, the agent calls `rent_instance`. If the initial deployment fails due to rate limiting, Mastra AI's retry mechanism kicks in automatically. You don't have to code that failure handling yourself. Alternatively, you can trigger `delete_instance` to gracefully clean up resources after an operation completes, ensuring your overall workflow always finishes cleanly.

Setup guide

Set up Vast.ai (GPU Rental Cloud API) 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 Vast.ai (GPU Rental Cloud API) 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: "vastai-gpu-rental-cloud-api-mcp-client",
  servers: {
    "vastai-gpu-rental-cloud-api-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Vast.ai (GPU Rental Cloud API) transactions"
);
console.log(result.text);

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Common questions about Vast.ai (GPU Rental Cloud API) MCP in Mastra AI

Your agent calls `search_offers` to query the marketplace using JSON criteria. If that search fails, Mastra AI automatically retries the operation with exponential backoff built into its workflow engine.

Yes. It uses `list_instances` to pull all active resource data. This allows your conditional branching logic to determine if an action is necessary before proceeding with the rest of the workflow.

The agent calls `rent_instance`. If the deployment fails for any reason, Mastra AI's core engine automatically retries the process until it succeeds or hits a defined limit.

It does. You can set up `requireToolApproval` so that if the workflow hits a critical state, it pauses and waits for a human to approve the next step before continuing.

The server touches instance metadata (GPU names, IDs). Because you're running complex, multi-step processes, reliable state management of these resources is critical for the workflow engine.

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