Bring Ffmpeg
to Vercel AI SDK
Learn how to connect Rendi to Vercel AI SDK and start using 11 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the 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.
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.
How it works
1. Subscribe to this server
2. Enter your Rendi API Key from your dashboard settings
3. Start processing media files from Claude, Cursor, or any MCP-compatible client
No more manual terminal work or cloud worker configuration. Your AI acts as a dedicated media engineer or technical content coordinator.
Who is this for?
- Content Engineers & Developers — quickly test FFmpeg parameters and monitor processing results without writing boilerplate code.
- Video Producers — automate the generation of previews and technical analysis via natural conversation.
- Operations Teams — streamline the retrieval of media metadata and monitor processing health directly within the chat.
Built-in capabilities (11)
Quickly convert a video to audio
Delete a file from Rendi storage
Analyze a media file using ffprobe
Generate a thumbnail from a video
Once completed, it provides the storage URL for output files. Get status of an FFmpeg command
Get details for a stored file
Get metadata and details for a specific file
List all submitted FFmpeg commands
List all files in Rendi storage
Run multiple chained FFmpeg commands
Returns a command ID to poll for status. Run a single FFmpeg command in the cloud
Why Vercel AI SDK?
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.
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TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
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Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Rendi integration everywhere
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Built-in streaming UI primitives let you display Rendi tool results progressively in React, Svelte, or Vue components
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Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Rendi in Vercel AI SDK
Rendi and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Rendi to Vercel AI SDK through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Rendi in Vercel AI SDK
The Rendi 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. All 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Vercel AI SDK only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Rendi for Vercel AI SDK
Every tool call from Vercel AI SDK to the Rendi MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can my AI automatically convert a video file into an MP3 audio track using Rendi?
Yes! Use the run_ffmpeg_command tool with the conversion parameters (e.g., 'ffmpeg -i input.mp4 output.mp3'). Your agent will execute the command in the cloud and return the result URL instantly.
How do I find my Rendi API Key?
Log in to your Rendi dashboard at rendi.dev, and your unique secret API key will be displayed on the main page or under account settings.
What is the format for chained FFmpeg commands?
Use the run_chained_ffmpeg_commands tool and provide an array of strings, where each string is a valid FFmpeg command. Rendi will execute them sequentially in a single processing job.
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.
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.
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.
createMCPClient is not a function
Install: npm install @ai-sdk/mcp