How to Use the Modelbit (ML Model Deployments) MCP in Cursor

Q: How do I add the Modelbit (ML Model Deployments) MCP Server to my Cursor project?

Create an .cursor/mcp.json file in your project root and define the server under the mcpServers key. Make sure to include your Modelbit workspace credentials in the environment variables so the editor can authenticate your requests.

Feed live predictions from Modelbit (ML Model Deployments) directly into your active code files using Cursor Agent.

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Connect Modelbit (ML Model Deployments) MCP to Cursor

Create your Vinkius account to connect Modelbit (ML Model Deployments) to Cursor 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 Cursor

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Generate code using live Modelbit (ML Model Deployments) outputs

The `get_inference` tool lets Cursor run actual prediction payloads against your Modelbit endpoints while you write code. This means your agent can fetch real model responses instead of generating stub data or mock objects. If you are building a feature that depends on a model's prediction score, Cursor uses this MCP Server to hit your live endpoint, see the exact payload structure, and write the parsing logic for you.

Test your model integrations without leaving Cursor

This MCP Server exposes the `get_inference` tool directly to Cursor's Composer and chat interfaces. You can instantly test how your local application logic handles different model outputs by running live predictions side-by-side with your code. If a model returns an unexpected confidence score, you can ask Cursor to run a test payload, analyze the raw output, and adjust your application's threshold logic on the fly.

Create test suites with real inference payloads

The `get_inference` tool allows your Cursor agent to fetch live test cases directly from your deployed Modelbit models. It uses these live runs to generate accurate unit tests and integration tests for your codebase. Instead of hardcoding obsolete mock payloads, your agent pulls the latest schema directly from your active deployment, ensuring your test suite stays in sync with your production models.

Setup guide

Set up Modelbit (ML Model Deployments) MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Modelbit (ML Model Deployments) MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Modelbit (ML Model Deployments) transactions." If the MCP tools are loaded correctly, Cursor will call the Modelbit (ML Model Deployments) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/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 Cursor

Create an `.cursor/mcp.json` file in your project root and define the server under the `mcpServers` key. Make sure to include your Modelbit workspace credentials in the environment variables so the editor can authenticate your requests.

Yes, Cursor's multi-file Composer can use this MCP Server to run live inferences while writing complex integration scripts. This allows the composer to inspect the model's actual output schema and build highly accurate data pipelines.

No, this server communicates with your hosted Modelbit deployments. It uses the `get_inference` tool to send data to your cloud endpoints, allowing you to test production-grade models from your local editor.

The server executes requests asynchronously. Cursor displays a loading state while waiting for the Modelbit endpoint to respond, ensuring your editor remains responsive even during heavy inference workloads.

Your model inputs and prediction outputs are sent directly to Modelbit over HTTPS. This MCP Server runs within Cursor's local environment, meaning your proprietary inference payloads never touch external caching layers or intermediate servers.

Use it with your favorite AI tools

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