Modelbit (ML Model Deployments) Connector for AI agents.
1 live capability
Run production ML models and get real-time predictions in your chat.
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Why people use Modelbit (ML Model Deployments)
Modelbit for Production ML Inference
This Connector lets you skip the copy-pasting. You just tell your agent to run the model on a specific set of data. It handles the request, gets the result, and gives you the answer in the chat. You get a direct line from your ML backend to your conversation.
What Vinkius changes
You get to run production ML models through a chat interface without writing any extra glue code.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Real-time Fraud Detection
A security analyst asks the agent to check a transaction.
- Real-world use case 02
Dynamic Sales Forecasting
A sales lead asks for a Q4 forecast.
- Real-world use case 03
Image Classification Testing
A researcher sends a pixel array to the agent.
Complete set · 1capability
The complete Modelbit (ML Model Deployments) capability set.
These are the exact actions your AI can choose when you ask it to work with Modelbit (ML Model Deployments).
01
1 capability in this set.
Part of 1 available through Modelbit (ML Model Deployments).
- 01 Capability
Get inference
Pass data to your ML models and get the computed output instantly. This lets your agent interact with your production inference endpoints.
Set up in minutes
One URL. Then ask Modelbit (ML Model Deployments) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Modelbit (ML Model Deployments) 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_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Modelbit (ML Model Deployments) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments) URL.
- Step 03
Save and start
Save the connection and enable Modelbit (ML Model Deployments) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"modelbit-ml-model-deployments": {
"url": "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments)
Open Agent mode in chat and ask: "Using Modelbit (ML Model Deployments), help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"modelbit-ml-model-deployments": {
"url": "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments)
Ask Copilot: "Using Modelbit (ML Model Deployments), help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"modelbit-ml-model-deployments": {
"url": "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments)
Open Cascade and ask: "Using Modelbit (ML Model Deployments), help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"modelbit-ml-model-deployments": {
"url": "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments)
Ask Cline: "Using Modelbit (ML Model Deployments), help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add modelbit-ml-model-deployments --transport http "https://edge.vinkius.com/vk_preview_U02S7R70GaoTP6dztOIIMDtmgDyuN2GTH3Or66r0/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 Modelbit (ML Model Deployments)
Ask Claude: "Using Modelbit (ML Model Deployments), show me...". 1 tools are ready
Where the request belongs
Work Modelbit can move forward.
This is for the ML engineer who's tired of writing boilerplate code just to see if a model works in a real workflow. It's for the data scientist who wants to demo results to a product team without building a full custom UI.
ML Engineer
Testing production inference paths in a chat interface on Tuesday afternoons to verify data flow.
Data Scientist
Showcasing model outputs to stakeholders without having to build a custom frontend or dashboard.
Product Manager
Prototyping features that rely on proprietary ML logic quickly to see if the logic holds up.
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep Modelbit connected.
-
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 Modelbit.
The practical details behind the request, access and result.
Can I use my existing Modelbit models with this Connector?
Yes, this connects your Modelbit workspace so your agent can call your already deployed models.
Does this work with my Python models?
It supports models deployed via Modelbit, including those built with Python, Scikit-learn, and PyTorch.
How do I ensure the agent uses the right version of my model?
You can specify exact version tags like 'v2' or 'latest' when asking your agent to run a prediction.
Can I send complex data to my ML models?
Yes, your agent can pass JSON objects and arrays directly to the model for inference.
Is this for production use?
It's designed for production-grade inference, allowing you to bridge the gap between your ML backend and your AI assistant.
Do I need to write any code to connect this?
No, once you subscribe and enter your workspace name, your agent handles the capability calls for you.
Can I specify which version of a model to use for inference?
Yes. When using the get_inference capability, you can provide an optional version string (e.g., 'v1', 'latest', or a specific tag) to target a precise deployment.
What format should the input data be in?
The get_inference capability accepts a data parameter which should be a JSON object or array, matching the input schema expected by your Modelbit deployment.
Is an API Key required for all models?
The MODELBIT_API_KEY is optional. It is only required if your Modelbit deployment is private. Public deployments only require the MODELBIT_WORKSPACE name.
One connection away
Give your agent a direct line to Modelbit.
Connect Modelbit once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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