Gumlet MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 12 tools to Create Collection, Create Video Upload, Delete Video, and more
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Gumlet through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
Ask AI about this App Connector for Vercel AI SDK
The Gumlet app connector for Vercel AI SDK is a standout in the Image Video category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Gumlet, 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 Gumlet MCP Server
Connect your Gumlet account to any AI agent and take full control of your video hosting and image optimization workflows through natural conversation.
The Vercel AI SDK gives every Gumlet tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 12 tools through 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
- Video Lifecycle — Manage the complete video lifecycle from creating new uploads and retrieving metadata to monitoring transcoding status
- Media Organization — Create and manage collections/folders programmatically to maintain a structured media library
- Visual Control — Automate thumbnail updates by selecting specific video frames or time offsets for perfect visual representation
- Optimization Insights — Monitor real-time video analytics, viewing metrics, and bandwidth usage for every asset in your account
- Image Source Management — List and manage image optimization sources and organization users to ensure high-fidelity delivery
The Gumlet MCP Server exposes 12 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.
All 12 Gumlet tools available for Vercel AI SDK
When Vercel AI SDK connects to Gumlet through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-hosting, image-optimization, cdn-delivery, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Add new folder
Upload new video
Remove video asset
Get profile details
Check video stats
Check video status
List image optimized sources
List team members
List folders
List video assets
Get active webhooks
Set thumbnail offset
Connect Gumlet to Vercel AI SDK via MCP
Follow these steps to wire Gumlet into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
npm install @ai-sdk/mcp ai @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the script
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Vercel AI SDK with the Gumlet MCP Server
Vercel AI SDK provides unique advantages when paired with Gumlet 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 Gumlet integration everywhere
Built-in streaming UI primitives let you display Gumlet 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
Gumlet + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Gumlet MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Gumlet in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Gumlet tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Gumlet capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Gumlet through natural language queries
Example Prompts for Gumlet in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Gumlet immediately.
"Create a new video upload in collection 'col_123' titled 'Annual Report 2026'."
"Check the transcoding status of video 'asset_987'."
"Show me the viewing stats for my latest product video."
Troubleshooting Gumlet MCP Server with Vercel AI SDK
Common issues when connecting Gumlet to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpGumlet + Vercel AI SDK FAQ
Common questions about integrating Gumlet 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.