How to Use the CoreWeave (AI GPU Cloud) MCP in Mastra AI

Automate complex infrastructure branching and failure recovery with CoreWeave (AI GPU Cloud) and Mastra AI.

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Connect CoreWeave (AI GPU Cloud) MCP to Mastra AI

Create your Vinkius account to connect CoreWeave (AI GPU Cloud) 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 CoreWeave (AI GPU Cloud) with Mastra AI

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Branching GPU workflows in Mastra AI

Write agents that decide whether to scale based on `list_capacity_claims` results. If capacity is low, your Mastra AI workflow can trigger a new `create_deployment` automatically. This turns your infrastructure into a reactive system. You build logic that handles resource shortages without waiting for a manual alert.

Automated cluster recovery with Mastra AI

Configure your agents to watch for errors using `query_logs`. If the agent detects a failure, it uses `update_cluster` or `delete_cluster` to cycle the node and restore service. You gain a self-healing layer for your GPU infrastructure. The system retries operations until the state matches your intended configuration.

Secure infrastructure orchestration in Mastra AI

Use the tool approval feature to confirm changes before they happen. Your agent prepares a `create_vpc` request, and you approve it inside your Mastra AI dashboard before execution. You retain control over your cloud footprint. Nothing changes on your CoreWeave account unless you give the final go-ahead.

Setup guide

Set up CoreWeave (AI GPU Cloud) 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 CoreWeave (AI GPU Cloud) 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: "coreweave-ai-gpu-cloud-mcp-client",
  servers: {
    "coreweave-ai-gpu-cloud-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent CoreWeave (AI GPU Cloud) transactions"
);
console.log(result.text);

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Common questions about CoreWeave (AI GPU Cloud) MCP in Mastra AI

You can define retry policies within your workflow engine. If a tool fails, the agent automatically attempts the action again with backoff.

Absolutely, just add the server to your agent configuration. You can then chain cluster creation and gateway setup into a single sequential operation.

It does. The server provides a standard interface that the framework parses into executable agent tools.

Your infrastructure definitions are stored in an ephemeral sandbox. No data persists outside of your active agent session.

The agent only sees the metadata for your clusters, VPCs, and deployment logs. It has no access to your billing information or root account credentials.

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