How to Use the UPYUN Developer Platform MCP in Mastra AI

Q: Can Mastra AI monitor resource usage with UPYUN Developer Platform?

Yes, it uses getserviceusage. This allows your workflow to check if the cloud bucket has enough capacity before attempting a major data upload or operation.

Q: Does UPYUN Developer Platform support structured file reading for Mastra AI?

The listdirectory tool provides the full directory structure. Your agent can analyze this output to determine if all necessary files are present before proceeding with a critical task.

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Connect UPYUN Developer Platform MCP to Mastra AI

Create your Vinkius account to connect UPYUN Developer Platform 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 UPYUN Developer Platform with Mastra AI

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Workflow Directory Mapping with MCP Server

Your workflow can map out dependencies by listing UPYUN directories using `list_directory`. This tells the agent exactly what assets exist before it tries to read or modify them. It's perfect for pre-flight checks. If one step fails, you know immediately which directory structure is broken just by calling this tool through your Mastra AI workflow.

State Management and File Updates

Mastra AI can guarantee state changes. Use `create_text_file` to generate required documentation, or call `delete_file` if a previous step failed and left garbage data behind. The workflow handles these modifications automatically. The agent doesn't just run; it modifies the cloud environment—it leaves assets exactly where they need to be for the next successful step.

Audit Cloud Resources in Mastra AI

Before committing a workflow, check resource limits. The `get_service_usage` tool provides critical quota data, while `get_file_info` verifies specific file details. This allows your conditional branching logic to handle resource constraints gracefully. Knowing the current usage ensures that retries or subsequent steps won't hit an unexpected throttling limit.

Setup guide

Set up UPYUN Developer Platform 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 UPYUN Developer Platform 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: "upyun-developer-platform-mcp-client",
  servers: {
    "upyun-developer-platform-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent UPYUN Developer Platform transactions"
);
console.log(result.text);

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Common questions about UPYUN Developer Platform MCP in Mastra AI

Mastra AI uses the MCP Server to manage cloud state. You can program conditional branches that execute `create_text_file` or `delete_file` based on the success or failure of previous steps.

Yes, it uses `get_service_usage`. This allows your workflow to check if the cloud bucket has enough capacity *before* attempting a major data upload or operation.

The `list_directory` tool provides the full directory structure. Your agent can analyze this output to determine if all necessary files are present before proceeding with a critical task.

This server handles file metadata and service usage metrics. It manages the existence, properties, and consumption reports of cloud assets.

It's built for reliability. Since your workflows require guaranteed execution, tools like `get_file_info` ensure that the data you plan to modify actually exists and has the expected properties.

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