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Rendi MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 11 tools to Convert Video To Audio, Delete File, Ffprobe, and more

Built by Vinkius GDPR 11 Tools SDK

The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Rendi through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.

Use with Your AI Agent

Ask AI about this App Connector for Vercel AI SDK

Open in ChatGPT Open in Claude Open in Perplexity

The Rendi app connector for Vercel AI SDK is a standout in the Industry Titans category — giving your AI agent 11 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

typescript
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 Rendi, list all available capabilities.",
    });
    console.log(text);
  } finally {
    await mcpClient.close();
  }
}

main();
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* 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 Rendi MCP Server

Connect your Rendi account to any AI agent and take full control of your cloud-based media processing and FFmpeg orchestration through natural conversation. Rendi provides a serverless platform for executing professional video and audio commands, allowing you to convert formats, generate thumbnails, and probe media metadata directly from your chat interface.

The Vercel AI SDK gives every Rendi tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 11 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

  • FFmpeg Command Orchestration — Run any standard FFmpeg command in the cloud programmatically without managing server infrastructure.
  • Media Format Intelligence — Convert videos to audio, generate GIFs, and create thumbnails directly from the AI interface using simple natural language.
  • Chained Workflow Control — Execute multiple media commands in a single request to automate complex processing pipelines.
  • FFprobe & Metadata Analysis — Analyze media files and retrieve technical metadata to ensure your assets meet professional standards.
  • Operational Monitoring — Track system activity and manage temporary cloud storage files using simple AI commands.

The Rendi MCP Server exposes 11 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 11 Rendi tools available for Vercel AI SDK

When Vercel AI SDK connects to Rendi through Vinkius, your AI agent gets direct access to every tool listed below — spanning ffmpeg, media-processing, video-transcoding, 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.

convert_video_to_audio

Quickly convert a video to audio

delete_file

Delete a file from Rendi storage

ffprobe

Analyze a media file using ffprobe

generate_thumbnail

Generate a thumbnail from a video

get_command_status

Once completed, it provides the storage URL for output files. Get status of an FFmpeg command

get_file_details

Get details for a stored file

get_file_info

Get metadata and details for a specific file

list_commands

List all submitted FFmpeg commands

list_files

List all files in Rendi storage

run_chained_ffmpeg_commands

Run multiple chained FFmpeg commands

run_ffmpeg_command

Returns a command ID to poll for status. Run a single FFmpeg command in the cloud

Connect Rendi to Vercel AI SDK via MCP

Follow these steps to wire Rendi into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run npm install @ai-sdk/mcp ai @ai-sdk/openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the script

Save to agent.ts and run with npx tsx agent.ts
04

Explore tools

The SDK discovers 11 tools from Rendi and passes them to the LLM

Why Use Vercel AI SDK with the Rendi MCP Server

Vercel AI SDK provides unique advantages when paired with Rendi through the Model Context Protocol.

01

TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box

02

Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Rendi integration everywhere

03

Built-in streaming UI primitives let you display Rendi tool results progressively in React, Svelte, or Vue components

04

Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency

Rendi + Vercel AI SDK Use Cases

Practical scenarios where Vercel AI SDK combined with the Rendi MCP Server delivers measurable value.

01

AI-powered web apps: build dashboards that query Rendi in real-time and stream results to the UI with zero loading states

02

API backends: create serverless endpoints that orchestrate Rendi tools and return structured JSON responses to any frontend

03

Chatbots with tool use: embed Rendi capabilities into conversational interfaces with streaming responses and tool call visibility

04

Internal tools: build admin panels where team members interact with Rendi through natural language queries

Example Prompts for Rendi in Vercel AI SDK

Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Rendi immediately.

01

"Analyze this media file for technical metadata: https://example.com/video.mp4"

02

"Convert this MP4 video to WebM format with H265 encoding and reduce the file size by 50%."

03

"Analyze the media properties of the uploaded video file and show me all codec and stream details."

Troubleshooting Rendi MCP Server with Vercel AI SDK

Common issues when connecting Rendi to Vercel AI SDK through the Vinkius, and how to resolve them.

01

createMCPClient is not a function

Install: npm install @ai-sdk/mcp

Rendi + Vercel AI SDK FAQ

Common questions about integrating Rendi MCP Server with Vercel AI SDK.

01

How does the Vercel AI SDK connect to MCP servers?

Import createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.
02

Can I use MCP tools in Edge Functions?

Yes. The AI SDK is fully edge-compatible. MCP connections work on Vercel Edge Functions, Cloudflare Workers, and similar runtimes.
03

Does it support streaming tool results?

Yes. The SDK provides streaming primitives like useChat and streamText that handle tool calls and display results progressively in the UI.