Clarifai (Vision AI) Connector for AI agents.
6 live capabilities
Run computer vision predictions and manage machine learning models from your chat.
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Why people use Clarifai (Vision AI)
Clarifai (Vision AI) for Computer Vision Inference
With this Connector, you just tell your agent to run a prediction. It handles the authentication, finds the right model, and gives you the classification results in your chat window immediately.
What Vinkius changes
You can manage your whole computer vision stack through chat instead of clicking through a dashboard.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Verifying a new model
A developer wants to see if a new model detects 'Person' correctly.
- Real-world use case 02
Auditing data consistency
A data scientist needs to see what concepts are tagging a specific dataset.
- Real-world use case 03
Managing compute limits
An engineer needs to know which apps are hitting limits.
Complete set · 6capabilities
The complete Clarifai (Vision AI) capability set.
These are the exact actions your AI can choose when you ask it to work with Clarifai (Vision AI).
01—03
3 capabilities in this set.
Part of 6 available through Clarifai (Vision AI).
- 01 Capability
List workflows
See the structural matching for your chained AI limits. Use this to verify how your models are tied together in a sequence.
- 02 Capability
List apps
See all your Clarifai apps and their global compute limits. This helps you keep your environment organized and audit your active compute.
- 03 Capability
List models
Get the computer vision parameters driving your AI features. Use this to see the structural details of your active models.
04—06
3 capabilities in this set.
Part of 6 available through Clarifai (Vision AI).
- 04 Capability
List concepts
See the semantic bounds and tags attached to your datasets. Use this to audit the textual concepts tagging your visual data.
- 05 Capability
Predict model
Run a validation inference and get the classification results for an image. It lets you see exactly what the neural network evaluated.
- 06 Capability
List datasets
Identify the physical bounds and data structures for your visual nodes. This makes it easy to browse your training data.
Set up in minutes
One URL. Then ask Clarifai (Vision AI) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Clarifai (Vision AI) 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_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Clarifai (Vision AI) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI) URL.
- Step 03
Save and start
Save the connection and enable Clarifai (Vision AI) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"clarifai-vision-ai": {
"url": "https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI)
Open Agent mode in chat and ask: "Using Clarifai (Vision AI), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"clarifai-vision-ai": {
"url": "https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI)
Ask Copilot: "Using Clarifai (Vision AI), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"clarifai-vision-ai": {
"url": "https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI)
Open Cascade and ask: "Using Clarifai (Vision AI), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"clarifai-vision-ai": {
"url": "https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI)
Ask Cline: "Using Clarifai (Vision AI), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add clarifai-vision-ai --transport http "https://edge.vinkius.com/vk_preview_Wl7k3LILBATZT8oR2K1AY33RSlJewMKygOPyhcuJ/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 Clarifai (Vision AI)
Ask Claude: "Using Clarifai (Vision AI), show me...". 6 tools are ready
Where the request belongs
Work Clarifai can move forward.
This is for the machine learning engineer who needs to check model performance quickly without a browser, or the data scientist auditing training sets for consistency.
ML Engineer
Monitors active compute brains and manages execution contexts on a daily basis.
Data Scientist
Audits datasets and concepts to ensure training data is consistent across apps.
Product Manager
Verifies AI output and vision logic during the prototyping phase of a new product.
Build the capability set
Add more capabilities.
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Bring your own AI
Change the model, client or framework. Keep Clarifai connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
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TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Clarifai.
The practical details behind the request, access and result.
Can Clarifai (Vision AI) MCP run image classifications?
Yes, it allows your AI agent to run predictions on images and return classification results, including confidence scores and labels, directly in your chat.
How do I use Clarifai (Vision AI) MCP to check my models?
You can ask your agent to list the models in your account. It will provide the structural parameters and vision features driving your active AI capabilities.
Can I manage Clarifai workflows with this Connector?
Yes, you can use it to retrieve and verify the structural matching for your chained AI limits, making it easy to see how your models are sequenced.
Does Clarifai (Vision AI) MCP support dataset auditing?
It does. You can ask your agent to identify your datasets and extract the semantic concepts tagging your visual data to ensure training consistency.
How do I connect Clarifai (Vision AI) MCP to Claude?
Once you subscribe on Vinkius, you'll get the connection details to add to your Claude app. Just provide your Clarifai Personal Access Token to get started.
What can Clarifai (Vision AI) MCP do for ML engineers?
It allows engineers to monitor active compute brains, audit datasets, and verify model predictions using natural language instead of manual dashboard navigation.
Can my agent run image predictions using custom models?
Yes. Provide the User ID, App ID, and Model ID, along with the input JSON (containing image URLs or bytes). The agent calls Clarifai's predict API and returns exactly what the AI detected, from tags to bounding boxes.
How can I audit the datasets being used in my Clarifai app?
Ask your agent to list datasets for a specific app. It returns the precise physical bounds mapping the image sets, helping you ensure that your training loop is using the correct data boundaries.
Can I see all active workflows in my organization?
Absolutely. Use the 'list_workflows' capability. Your agent will pull the chained AI limits, showing you composed computational blocks that tie multiple neural networks together for complex visual tasks.
One connection away
Give your agent a direct line to Clarifai.
Connect Clarifai once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available