How to Use the UPYUN Developer Platform MCP in Vercel AI SDK

Q: How do I check UPYUN directory contents using UPYUN Developer Platform and Vercel AI SDK?

You call listdirectory through your AI client. Because it's compatible with streaming, the results appear piece by piece in the frontend UI as soon as UPYUN processes the folder structure.

Q: Can I track file usage metrics using UPYUN Developer Platform and Vercel AI SDK?

Yep. You use getserviceusage to get current bucket utilization details. This data is exposed through the MCP Server, letting your streaming UI show resource health in real-time.

Q: Does the UPYUN Developer Platform support file creation with Vercel AI SDK?

Absolutely. The createtextfile tool lets you build functionality that generates and uploads new text assets to UPYUN, all visible in your streaming client.

Q: What data types does this MCP Server touch for the Vercel AI SDK?

This server manages file metadata (via getfileinfo) and usage metrics. It primarily deals with structured text data used for cloud assets and service reports.

Real-time cloud interaction for Vercel AI SDK frontends.

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Connect UPYUN Developer Platform MCP to Vercel AI SDK

Create your Vinkius account to connect UPYUN Developer Platform to Vercel AI SDK 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 Vercel AI SDK

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Stream Directory Contents via Vercel AI SDK

You can stream the contents of any UPYUN directory using `list_directory`. This lets your frontend show a user the folder structure as it's being analyzed, instead of waiting for one big JSON blob. It works perfectly with edge functions. The client gets an immediate update every time you call `list_directory`, making complex file system lookups feel instantaneous to the end user.

Manage Files in UPYUN Developer Platform

Build actions that modify cloud content directly into your UI. Use `create_text_file` to generate new assets, or `delete_file` when a resource is no longer needed. These operations appear live in the stream, giving users instant feedback on data changes. Handling file lifecycles—from creation to removal—is simple with these tools, making your AI client feel like it's operating directly inside UPYUN.

Check Usage and Metadata with MCP Server

Before writing data, you need context. Use `get_file_info` to check metadata or call `get_service_usage` to see how much quota is left in the UPYUN bucket. This prevents your application from failing due to unexpected limits. Knowing the file size and ownership details beforehand keeps the user experience smooth. You can show this resource data right alongside the generated text.

Setup guide

Set up UPYUN Developer Platform MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all UPYUN Developer Platform tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent UPYUN Developer Platform transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about UPYUN Developer Platform MCP in Vercel AI SDK

You call `list_directory` through your AI client. Because it's compatible with streaming, the results appear piece by piece in the frontend UI as soon as UPYUN processes the folder structure.

Yep. You use `get_service_usage` to get current bucket utilization details. This data is exposed through the MCP Server, letting your streaming UI show resource health in real-time.

Absolutely. The `create_text_file` tool lets you build functionality that generates and uploads new text assets to UPYUN, all visible in your streaming client.

This server manages file metadata (via `get_file_info`) and usage metrics. It primarily deals with structured text data used for cloud assets and service reports.

It's designed for real-time streaming, which means the user sees the outcome immediately. You can confidently build interactive products where the data appears live as your agent works.

Use it with your favorite AI tools

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

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