Rendi Connector for AI agents.
11 live capabilities
Process and transcode video files using cloud-based FFmpeg.
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Why people use Rendi
Rendi Video Transcoding: Stop wasting time on manual FFmpeg syntax
With Rendi, you just tell your AI client what you want to happen. It handles the cloud execution and gives you the final file link. You get professional results without the technical friction.
What Vinkius changes
You get cloud-based media processing through a simple chat interface.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Convert tutorial videos to audio
A creator asks their agent to extract the audio from a long tutorial video.
- Real-world use case 02
Bulk metadata auditing
An ops lead needs to check the bitrates of 50 videos.
- Real-world use case 03
Automated preview generation
A producer wants thumbnails for a new series.
Complete set · 11capabilities
The complete Rendi capability set.
These are the exact actions your AI can choose when you ask it to work with Rendi.
01—04
4 capabilities in this set.
Part of 11 available through Rendi.
- 01 Capability
Convert video to audio
Turn a video file into a clean audio track. This is perfect for extracting sound from a tutorial.
- 02 Capability
Delete file
Remove a file from your Rendi storage. Use this to keep your cloud space clean and organized.
- 03 Capability
Ffprobe
Analyze a media file to see its technical details. It pulls out bitrates, resolutions, and codecs instantly.
- 04 Capability
Get command status
Check the progress of an FFmpeg command. It gives you the final storage URL once the work is done.
05—08
4 capabilities in this set.
Part of 11 available through Rendi.
- 05 Capability
Get file details
See the specific details of a file in your storage. Use this to find out where a file is located.
- 06 Capability
Get file info
Grab the metadata and info for a specific file. This is useful for verifying file properties quickly.
- 07 Capability
List commands
See every FFmpeg command you've submitted. Use this to keep track of your ongoing processing jobs.
- 08 Capability
List files
View every file currently in your Rendi storage. This gives you a quick overview of your cloud assets.
09—11
3 capabilities in this set.
Part of 11 available through Rendi.
- 09 Capability
Run chained ffmpeg commands
Execute a series of FFmpeg commands in one go. This is the best way to automate multi step media pipelines.
- 10 Capability
Run ffmpeg command
Send a single FFmpeg command to the cloud. This starts a new job and gives you a command ID to track.
- 11 Capability
Generate thumbnail
Create a still image from a video. This helps you quickly get preview images for your content.
Set up in minutes
One URL. Then ask Rendi to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Rendi from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Rendi, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Rendi for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Rendi URL.
- Step 03
Save and start
Save the connection and enable Rendi in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"rendi": {
"url": "https://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Rendi
Open Agent mode in chat and ask: "Using Rendi, help me...". 11 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"rendi": {
"url": "https://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Rendi
Ask Copilot: "Using Rendi, help me...". 11 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"rendi": {
"url": "https://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Rendi
Open Cascade and ask: "Using Rendi, help me...". 11 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"rendi": {
"url": "https://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Rendi
Ask Cline: "Using Rendi, help me...". 11 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add rendi --transport http "https://edge.vinkius.com/vk_preview_yLT1nhPB29SIqqpsgoNP1Ev6sJDTpO5q0f2OjGk5/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Rendi
Ask Claude: "Using Rendi, show me...". 11 tools are ready
Where the request belongs
Work Rendi can move forward.
The content engineer tired of writing long FFmpeg strings, the video producer who needs quick previews, and the ops person who needs to audit media metadata at scale.
Content Engineer
Testing FFmpeg parameters and monitoring results without writing boilerplate code.
Video Producer
Automating the generation of previews and technical analysis via natural conversation.
Operations Team
Retrieving media metadata and monitoring processing health directly within a chat.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Optimize images and stream video with automatic format conversion, lazy loading, and CDN delivery that loads pages faster.
Cloudinary
Manage media assets via Cloudinary. track resources, monitor usage, and automate transformations directly from any AI agent.
Kraken.io
Optimize, compress, and resize images via URL or direct upload using the Kraken.io API.
Transloadit
Versatile file uploading and processing API.
imgix (Real-time Image Processing)
Optimize and transform images via imgix. manage CDN sources, purge assets, and monitor origin connections.
Convertio
Enable your AI agent to convert images, documents, audio, and video files across hundreds of formats using the Convertio API.
Bring your own AI
Change the model, client or framework. Keep Rendi connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Rendi.
The practical details behind the request, access and result.
Can Rendi help me convert videos to different formats?
Yes, Rendi lets you change video formats easily. You can ask your AI client to turn an MP4 into a WebM or any other format supported by the cloud.
How do I get thumbnails from my videos?
You can just ask your AI client to generate a thumbnail for a specific video. Rendi handles the extraction and gives you the link to the image.
Can I use Rendi to check video bitrates?
Yes, Rendi can pull all the technical specs of a file. Just ask your agent to analyze the media properties to see codecs and streams.
Does Rendi handle long video files?
Yes, because it runs in the cloud, it handles heavy processing without slowing down your own computer. It's built for professional media work.
How does Rendi handle multiple steps at once?
You can tell your agent to run a chain of commands. Rendi will process them in order and give you the final result automatically.
Can my AI automatically convert a video file into an MP3 audio track using Rendi?
Yes! Use the run_ffmpeg_command capability 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 capability and provide an array of strings, where each string is a valid FFmpeg command. Rendi will execute them sequentially in a single processing job.
One connection away
Give your agent a direct line to Rendi.
Connect Rendi once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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