Wasabi MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Wasabi through the Vinkius and every tool is available as a typed function — ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token — get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Wasabi, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Wasabi MCP Server
Connect your Wasabi Hot Cloud Storage account to any AI agent and take full control of your cloud assets through natural conversation.
The Vercel AI SDK gives every Wasabi tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 10 tools through the Vinkius and stream results progressively to React, Svelte, or Vue components — works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Bucket Management — List all storage buckets, create new ones, or delete obsolete containers in your account
- Object Discovery — Browse and list files (objects) stored within specific buckets, including sizes and last modified dates
- Data Integrity — Enable and check bucket versioning to protect against accidental file overwrites or deletions
- Access Control — Audit permissions and retrieve Access Control Lists (ACL) for specific files to ensure security
- Data Residency — Verify the physical geographic region where your data is hosted for compliance needs
- Cleanup Tasks — Identify fractured file uploads that consume storage and permanently delete obsolete assets
The Wasabi MCP Server exposes 10 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Wasabi to Vercel AI SDK via MCP
Follow these steps to integrate the Wasabi MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 10 tools from Wasabi and passes them to the LLM
Why Use Vercel AI SDK with the Wasabi MCP Server
Vercel AI SDK provides unique advantages when paired with Wasabi through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime — same Wasabi integration everywhere
Built-in streaming UI primitives let you display Wasabi tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Wasabi + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Wasabi MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Wasabi in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Wasabi tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Wasabi capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Wasabi through natural language queries
Wasabi MCP Tools for Vercel AI SDK (10)
These 10 tools become available when you connect Wasabi to Vercel AI SDK via MCP:
create_storage_bucket
Provide a globally unique lower-kebab-case name. Creates a new high-availability storage bucket in the configured Wasabi region
delete_bucket_object
This action is irreversible. Permanently deletes a specific file from a bucket
delete_storage_bucket
Note: The bucket must be completely empty first. This action is irreversible. Permanently removes an empty storage bucket
enable_bucket_versioning
Activates object versioning for a bucket
get_bucket_datacenter_location
Retrieves the physical geographic region where a bucket is hosted
get_bucket_versioning_status
Checks if object versioning is enabled for a bucket
get_object_access_control
Retrieves the access control list (ACL) for a specific file
list_bucket_objects
Returns file keys, sizes, and last modified dates. Lists the files (objects) stored within a specific bucket
list_pending_multipart_uploads
Lists incomplete multipart uploads in a bucket
list_storage_buckets
Lists all Wasabi storage buckets visible to the authenticated IAM user
Example Prompts for Wasabi in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Wasabi immediately.
"List all my storage buckets in Wasabi."
"What files are inside the 'backups-2026' bucket?"
"Is versioning enabled for my 'user-data-prod' bucket?"
Troubleshooting Wasabi MCP Server with Vercel AI SDK
Common issues when connecting Wasabi to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpWasabi + Vercel AI SDK FAQ
Common questions about integrating Wasabi MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.Connect Wasabi with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Wasabi to Vercel AI SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.