Modelbit MCP. Run proprietary ML models directly from chat logic.

When you use get_inference, your AI client calls any deployed Modelbit machine learning model right from your agent. You pass structured data—whether it's a complex array of pixels or specific parameters—and immediately get computed predictions back in a clean, usable format. This tool lets you run proprietary ML logic inside an LLM chat without having to write custom wrapper code just for testing.

This server executes production-grade models built using diverse frameworks. You don't care if your model was written in PyTorch, Scikit-learn, or plain Python; the get_inference tool handles running it all through a single call. This means you can test out sophisticated data science concepts directly within your chat flow, treating the ML model like just another function available to your agent.

Passing structured data is key here. You're not sending vague text prompts; you're giving the model exact inputs. You can send complex JSON objects or entire arrays of values, and the tool processes that structure directly. This capability means if your workflow requires analyzing a specific set of coordinates or processing multiple related data points simultaneously, the agent handles it by feeding those structured payloads straight into the deployed model.

The system guarantees reproducibility through version control. When you call get_inference, you specify exact model versions or tags—say, 'v2' or maybe 'latest'—and that ensures the results are always consistent and predictable. You won't run into the headache of an unpredictable output because the underlying model definition is locked down for your session.

When the computation finishes, you receive computed results instantly. The agent doesn't get a messy block of text; it gets the final, calculated output in a structured format that your client can read and act on immediately. This direct access to clean data means you can build complex decision-making paths within your conversational AI, making those predictions part of the ongoing dialogue.

The get_inference tool handles everything from execution across multiple ML frameworks—Python, PyTorch, Scikit-learn—to accepting highly structured inputs like JSON arrays. It ensures that every call is versioned and returns a clean, computable output ready for your agent to use in its next step. You're essentially making the model an active component of your workflow, not just something you mention passing data to.

This means if your application needs deep ML analysis—say, predicting stock movement based on historical JSON inputs or classifying images using a PyTorch-trained network—you don't need a separate API layer. You simply let your agent call get_inference, pass the structured payload, and get the computed prediction right back into the chat session.

It cuts out layers of integration complexity, letting you focus solely on the logic that needs to happen.