Modelbit MCP for AI. Run proprietary ML models directly from chat logic.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
get_inference calls any deployed Modelbit machine learning model directly from your AI agent. You pass structured data—like complex JSON arrays or specific parameters—and immediately receive computed predictions.
It eliminates the need to build custom wrapper code just to test proprietary ML logic inside an LLM chat.
What AI agents can do with Modelbit (ML Model Deployments) Automation
Get inference
Calls a deployed Modelbit machine learning model with specific input parameters, returning structured predictions or computed outputs.
The tool runs models built with various frameworks (Python, PyTorch, Scikit-learn) through a single call.
You send complex JSON objects or arrays directly to the model for processing.
Specify exact model versions or tags (e.g., 'v2' or 'latest') ensuring results are always reproducible.
The agent gets the final, calculated output from the model instantly in a structured format.
Ask an AI about this
Waiting for input…
What AI agents can do with Modelbit (ML Model Deployments) MCP Server: 1 Tool
The `get_inference` tool allows your AI client to execute deployed machine learning models, passing structured data and getting computed results back instantly.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Modelbit (ML Model Deployments) on VinkiusGet Inference
Calls a deployed Modelbit machine learning model with specific input parameters, returning structured predictions or computed outputs.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Modelbit integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Modelbit (ML Model Deployments), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Modelbit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Running ML logic today means jumping between dashboards and API calls., Solved with Vinkius AI Gateway
Right now, if your AI agent needs a prediction, you're out of luck. You have to leave the chat, jump into the Modelbit dashboard (or whatever service hosts the model), manually input parameters, hit 'run,' copy the resulting JSON/number, and paste it back into the conversation. It’s slow, error-prone, and breaks the flow.
With this MCP server, that whole process vanishes. You tell your agent to run `get_inference`. The agent handles calling Modelbit, passing the complex data, waiting for the prediction, and presenting the final result—all within one conversation thread.
Modelbit (ML Model Deployments) MCP Server: get_inference
The main pain point that disappears is the manual handoff of data. You no longer need to write custom Python or Javascript glue code in your application layer just to manage the input/output between your agent and the model's API.
You just call `get_inference`. The server handles the communication protocol, versioning, and structured data passing. It lets you treat a complex ML pipeline like it's just another function call.
What your AI can actually do with this
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.
019e5d36-d263-734a-bbfe-d289a41c27f0 
Here's how it actually works
The bottom line is: your AI client runs complex ML logic without needing custom API wrappers or external code execution.
Subscribe to the Modelbit server and provide your workspace credentials.
Tell your AI client (your agent) to run get_inference, specifying the exact model name, version tag, and the input data payload.
The agent executes the call, and you receive the computed prediction or result directly in the chat interface.
Who is this actually for?
ML Engineers who are tired of writing boilerplate glue code just to connect a model demo to an agent; Data Scientists who need to prove out proprietary algorithms within a chat interface; Product Teams prototyping AI features that rely on deep, custom machine learning logic.
Integrates production-grade models into AI workflows by calling get_inference directly, skipping the need for custom API wrappers.
Tests and showcases model outputs in an interactive chat environment, passing complex data structures to prove out algorithms.
Rapidly prototypes AI features that require proprietary ML logic by defining inputs and analyzing get_inference results within the agent interface.
What Changes When You Connect
No custom wrapper code. You connect your agent to production models and run them immediately via get_inference. This saves time building boilerplate API integration layers.
Reproducible outputs are guaranteed. By specifying model versions or tags (e.g., 'v2'), you ensure the results never change unexpectedly, which is critical for testing.
Handles complex data natively. You can pass structured inputs—like arrays of pixel values or multi-field JSON objects—straight to the model, letting it do the math.
Supports major frameworks. Whether your model uses PyTorch, Scikit-learn, or pure Python, Modelbit exposes it through one unified endpoint for get_inference.
Test proprietary logic instantly. You can showcase custom ML algorithms inside an AI chat interface without having to deploy a separate web service just for testing.
See it in action
Detecting Fraud in Transactions
A user asks, 'What's the risk score on this transaction?' The agent runs get_inference using the 'fraud_detection' model. It passes the transaction details (amount, time, region) as a JSON object and gets back a clear risk assessment, like 'low risk (score: 0.02).'
Classifying Satellite Imagery
You need to know what's in an image. The agent uses get_inference on the 'image_classifier' model, passing an array of pixel values. It returns a specific identification and confidence level (e.g., 'high-resolution satellite imagery with 98% confidence').
Forecasting Sales Revenue
A PM asks the agent to predict sales for Q4 North region. The agent calls get_inference on the 'sales_forecast' model, feeding it {'region': 'north', 'month': 12}. It immediately replies with a calculated revenue target: '$450,000.'
Validating Product Data
You give the agent raw data and ask it to validate it against your proprietary schema. The agent uses get_inference on a validation model, passing the JSON record. It returns whether the data is valid or lists exactly which fields failed.
The honest tradeoffs
Treating ML as general knowledge
Asking the agent, 'What's a good sales forecast?' The LLM will give a generic answer because it doesn't know your company’s data or model.
You must explicitly call get_inference. You need to pass the specific data and tell the tool which proprietary model (e.g., 'sales_forecast') to run on that data.
Passing raw text when structured data is needed
Trying to get a risk score by just saying, 'This transaction seems risky.' The tool doesn't know the exact parameters required for your model.
Always structure your input as JSON. Pass all necessary details (region, amount, time) in the data payload when calling get_inference.
Relying on default versions
Running a critical model and hoping it uses 'the latest one.' If Modelbit updates the backend, your results could change unexpectedly.
Always specify the exact version or tag (e.g., v2 or 'stable') when calling get_inference. This guarantees reproducible outputs.
When It Fits, When It Doesn't
Use this server if you have existing, deployed machine learning models and need to expose their results to your AI agent’s reasoning flow. Specifically, if your task requires passing structured data (JSON) into a proprietary model endpoint for computation—like classification, forecasting, or scoring—this is the tool. Don't use it if your logic is simple enough that standard LLM prompting works; don't force get_inference just because you can. Also, don't use it if your models are currently only running in Jupyter notebooks and haven't been fully deployed to Modelbit endpoints yet—the model has to be live first.
Questions you might have
How do I set up get_inference for my first time? +
You subscribe to the server and enter your Modelbit Workspace name. If your models are private, you'll also need to provide your API Key in the setup panel.
Does get_inference support different model types (PyTorch vs Scikit-learn)? +
Yes. The server is built to connect to any deployed ML framework—Python, PyTorch, Scikit-learn, etc.—as long as it's exposed via Modelbit.
What data format must I use with get_inference? +
You must pass structured data. This means using JSON objects or arrays for the input payload when calling the tool, not just plain text.
Can I test a model version before deploying it? +
The get_inference tool supports versioning. You can specify tags (like 'v2') to ensure you are always testing against a known, stable model iteration.
What happens if an ML model fails or encounters bad data when I use get_inference? +
The agent receives a structured error message. Modelbit reports specific failure codes and stack traces, telling you exactly which part of the input failed. This lets your AI client retry the call with corrected parameters.
How do I secure my model calls when using get_inference in production? +
You must use a private Modelbit API Key for secure deployments. By entering this key, you restrict access to your specific workspace and models. This keeps proprietary logic protected from unauthorized client connections.
Are there limitations on the size of data I can pass to get_inference? +
While Modelbit handles complex JSON and arrays, input size depends on the model's specific requirements and general platform limits. For extremely large datasets, consider chunking the data or using a dedicated data pipeline before calling get_inference.
What factors affect the latency when I run get_inference? +
Latency is determined by three things: network speed, Modelbit's processing time, and the model itself. Complex models or massive input arrays will naturally take longer to compute than simple predictions.
Can I specify which version of a model to use for inference? +
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.
What format should the input data be in? +
The get_inference tool 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.
We've already built the connector for Modelbit. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 1 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.