How to Use the Modelbit (ML Model Deployments) MCP in VS Code Copilot

Q: How do we share the Modelbit (ML Model Deployments) MCP Server config in VS Code Copilot?

Create a .vscode/mcp.json file in your repository root and add the server configuration. When team members open the project in VS Code Copilot, the editor automatically loads the prediction tool for everyone.

Q: Can VS Code Copilot run bulk predictions using Modelbit (ML Model Deployments)?

Yes, you can pass arrays of data to the getinference tool through the chat interface. Copilot will process the batch, query the endpoint, and format the results into a clean table or JSON block.

Share live Modelbit (ML Model Deployments) endpoints across your engineering team directly inside VS Code Copilot.

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Connect Modelbit (ML Model Deployments) MCP to VS Code Copilot

Create your Vinkius account to connect Modelbit (ML Model Deployments) to VS Code Copilot and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Modelbit (ML Model Deployments) with VS Code Copilot

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Share Modelbit (ML Model Deployments) tools across your team

The `get_inference` tool allows your entire engineering team to query active ML models directly from VS Code Copilot. By committing this MCP Server configuration to your repository, every developer gets instant access to the same production endpoints. This shared setup ensures that front-end and back-end developers can test model behavior without needing Python environments or local model weights installed on their workstations.

Validate your JSON payloads inside VS Code Copilot

This MCP Server uses the `get_inference` tool to let Copilot validate your application's outgoing payloads against your actual Modelbit deployment. You can highlight a block of data-prep code and ask Copilot to test it against the live endpoint. If the model rejects the input due to a missing feature or an incorrect data type, Copilot catches the error in your chat window and suggests the exact code fix.

Prototype ML integrations in Copilot Chat

The `get_inference` tool lets you mock up new application features by querying your Modelbit models in real-time. Copilot uses this MCP connection to simulate user interactions, send the resulting data to your model, and display the prediction. This interactive loop lets you verify your product logic before writing a single line of backend integration code, saving days of engineering overhead.

Setup guide

Set up Modelbit (ML Model Deployments) MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the Modelbit (ML Model Deployments) MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the Modelbit (ML Model Deployments) tools listed. Try asking: "List my recent Modelbit (ML Model Deployments) transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

{
  "mcpServers": {
    "modelbit-ml-model-deployments-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Modelbit (ML Model Deployments) MCP in VS Code Copilot

Create a `.vscode/mcp.json` file in your repository root and add the server configuration. When team members open the project in VS Code Copilot, the editor automatically loads the prediction tool for everyone.

No, you do not need Python or any ML libraries installed locally. This MCP Server communicates directly with Modelbit's cloud endpoints, allowing VS Code Copilot to run inferences purely over HTTPS.

Yes, you can pass arrays of data to the `get_inference` tool through the chat interface. Copilot will process the batch, query the endpoint, and format the results into a clean table or JSON block.

You need VS Code version 1.96 or higher along with the latest GitHub Copilot extension to support custom configuration files.

All inference payloads are transmitted directly from your local VS Code instance to your isolated Modelbit workspace. The server does not log or store your input features, ensuring your proprietary data remains secure.

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