MCP For Claude Marketplace
Modelbit (ML Model Deployments)

Modelbit (ML Model Deployments) MCP Connector for Claude

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Deploy and call machine learning models directly from your AI agent using Modelbit's inference endpoints.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Connect your Modelbit workspace to any AI agent to run production-grade machine learning models through natural conversation.

What you can do

  • Model Inference — Call any deployed ML model (Python, Scikit-learn, PyTorch, etc.) using the get_inference tool to get real-time predictions.
  • Version Control — Specify exact model versions or tags (like 'latest' or 'v2') to ensure consistent and reproducible outputs.
  • Data Integration — Pass complex JSON objects or arrays directly to your models and receive computed results instantly.
  • Seamless MLOps — Bridge the gap between your data science notebooks and your AI assistant's reasoning capabilities.

How it works

  1. Subscribe to this server
  2. Enter your Modelbit Workspace name
  3. (Optional) Enter your Modelbit API Key for private deployments
  4. Start getting predictions from your models in Claude, Cursor, or any MCP-compatible client

Who is this for?

  • Data Scientists — test and showcase model outputs directly within an AI chat interface
  • ML Engineers — integrate production models into AI-driven workflows without building custom glue code
  • Product Teams — prototype AI features that rely on custom proprietary machine learning logic
machine-learningmlopsinferencemodel-deploymentpython-models

1 tools expose this connector's capabilities to your AI agent.

get_inference

Use this to pass data to your ML models and receive the computed output. Call a deployed Modelbit machine learning model

See how to talk to your AI agent using Modelbit (ML Model Deployments).

Call the 'sales_forecast' model with data: {'region': 'north', 'month': 12}.

I've sent the request to the 'sales_forecast' deployment. The model predicts a revenue of $450,000 for the North region in December.

Get an inference from 'image_classifier' version 'v2' for this input array of pixel values.

Using version 'v2' of 'image_classifier', the model has identified the object as 'high-resolution satellite imagery' with 98% confidence.

Run the 'fraud_detection' model on the latest transaction data.

I've executed the `get_inference` tool for 'fraud_detection'. The model flagged the transaction as 'low risk' (score: 0.02).

Yes. When using the `get_inference` tool, you can provide an optional `version` string (e.g., 'v1', 'latest', or a specific tag) to target a precise deployment.

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