Baseten MCP. MLOps Control and Model Inference on Demand
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Baseten helps you manage and run ML models directly through your AI agent. You can list available models, inspect deployment status, check environment secrets, and execute real-time inference predictions without leaving your chat window or IDE.
What your AI agents can do
Get deployment
Fetch explicit status and details for a specific running deployment.
Get model
Retrieve the core configuration information for a named Baseten model.
List deployments
Get a list of active deployment instances associated with a particular model.
Retrieves a catalog of every Baseten model currently managed by the account.
Gets explicit, detailed information about any running inference deployment instance.
Executes a serverless model prediction by feeding structured data (like tensors or JSON) directly to the deployed weights.
Enumerates all active, secured keys and credentials stored in the workspace without exposing their actual values.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Baseten MCP: 6 Tools for MLOps Control
These tools let you manage the full lifecycle of deployed ML assets—from listing available models to running live inference predictions.
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 Baseten on Vinkius019d7558get deployment
Fetch explicit status and details for a specific running deployment.
019d7558get model
Retrieve the core configuration information for a named Baseten model.
019d7558list deployments
Get a list of active deployment instances associated with a particular model.
019d7558list models
List all Baseten models that have been registered in the workspace.
019d7558list secrets
Show all environment secrets available for use, without revealing their private values.
019d7558predict
Sends structured input (tensor or JSON) to a live deployment endpoint to get an immediate inference result.
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 Baseten, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ 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 Baseten. 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
Works with 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 server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The painful way to check your ML deployment status today
You know the drill: You need to confirm if a new machine learning feature is deployed, and you have to jump through hoops. First, log into the dashboard, find the model name, then click on 'Deployments,' hoping the right replica status shows up. If that's wrong, you copy the deployment ID, switch to a separate CLI window, and run a `get_deployment` command just to verify the autoscaling settings. It’s slow, it involves five different clicks or commands, and you risk mixing up which version you're actually looking at.
With this MCP, your agent handles all that friction for you. You ask what you need—say, 'Check the status of Model X.' The system finds the model, checks its deployments, verifies the replica count, and gives you a clean answer right here in the chat. It's immediate operational visibility without leaving your workflow.
Using Baseten MCP to run inference predictions
Before this, running even a single test prediction meant setting up a local Python environment or writing an API call script just to pass the data payload. You had to deal with boilerplate code and managing input/output schemas every time.
Now, you simply ask your agent to `predict`. You give it the text or JSON, and it handles all the connection setup, schema validation, and execution across the targeted model instance. You get a clean result object back instantly.
What you can do with this MCP connector
ML model infrastructure is messy stuff. You shouldn't have to switch between a terminal, a dashboard, and an API client just to run a test prediction. This MCP lets you treat complex ML operations like natural conversation. Instead of manually checking deployment statuses or looking up configuration files, your agent handles it all.
You can list available models, check detailed deployment states, audit workspace secrets, or push data directly for real-time predictions. It's pure MLOps control flowing through a chat interface. Because you're working with volatile infrastructure and sensitive keys, Vinkius manages everything inside an isolated sandbox, ensuring your credentials pass through its zero-trust proxy so they never sit unprotected on disk.
This lets your agent act like a true Machine Learning Operator, handling the GPU lifecycle for you.
019d7558-a9f9-70f4-aef5-95adbac62678 
How Baseten MCP Works
- 1 First, connect your AI client to this MCP using your Baseten API key.
- 2 Next, tell your agent what you need: 'List all models' or 'Predict sentiment for this text.'
- 3 The agent handles the complex calls—whether it's fetching
get_modeldetails or running a prediction viapredict—and returns the result directly.
The bottom line is you get full ML-Ops control over your inference nodes without leaving your preferred AI client.
Who Is Baseten MCP For?
ML Engineers, DevOps/SREs, and AI Researchers who are done jumping between command lines, dashboards, and notebooks. You need a single pane of glass view for model lifecycle management.
Checks get_model details to validate configuration before running test payloads against new deployments.
Uses list_deployments and get_deployment to audit running resources, verify replica counts, and confirm autoscaling rules from their IDE.
Runs multiple predictions using the predict tool across different model versions to compare performance metrics rapidly.
What Changes When You Connect
- Inspect the full state of your system by using
list_modelsto see every registered model, or runningget_modelfor deep configuration details. - Immediately test new ideas. Instead of setting up a local Python environment, you can execute predictions with the
predicttool and push payloads directly to GPU weights. - Audit your entire infrastructure by calling
list_deployments, which gives you visibility into all active inference bounds matching specific models. - Stay secure while working on sensitive projects. Use
list_secretsto see what environment keys are available without ever viewing the actual plaintext values. - Streamline testing for ML Engineers: Combine
get_deploymentwithpredictto validate a model's performance against its current live state in two steps.
Real-World Use Cases
Validating new production models
The SRE needs to confirm that the latest version of the 'Image Classifier' is running with the correct autoscaling rules. They prompt their agent, which then uses list_deployments and get_deployment to deliver the exact replica state from their core IDE.
Cross-checking environment permissions
The AI Researcher needs to know if a specific credential, like an external API key, is available for use. They ask the agent, which then calls list_secrets and confirms its availability without showing the actual secret value.
Quick performance spot check
A developer has a small batch of text data they need to pass through the 'Sentiment Analyzer' model. Instead of writing a script, they tell their agent to use predict, providing the input data and getting the score back in real-time.
Discovering available assets
A new ML team member needs to know what models are currently hosted. They prompt the agent, which uses list_models to provide a clean catalog of all primary managed resources.
The Tradeoffs
Using local notebooks for production checks
Writing up a complex Jupyter notebook just to test if an autoscaling rule is working or to validate the latest model version's input/output schema.
→
Use list_deployments and then get_deployment to audit the live resource state. If you need data, use predict directly in your agent instead of running a local script.
Manually logging credentials
Copying API keys or environment variables into chat logs or personal documents for later reference.
→
Only use the agent to call list_secrets. The tool confirms that secrets are provisioned and available in the isolated ecosystem without ever exposing their actual values.
Trying to guess deployment IDs
Guessing which model ID is running the latest version or which deployment instance matches a specific environment.
→
list_models gives you the starting point, and list_deployments lets you filter those models down to only active instances.
When It Fits, When It Doesn't
Use this MCP if your primary job is validating, inspecting, or executing inference against a known ML model infrastructure. Specifically, use it when you need to audit deployment status (get_deployment) or run immediate tests (predict). Don't use it if you are building the underlying training pipeline or managing CI/CD triggers; those require dedicated orchestration tools. If all you need is a simple list of available models and nothing else, list_models works. But when you combine model discovery with live execution—that’s where this MCP shines.
Common Questions About Baseten MCP
How do I list models with Baseten MCP? +
You use list_models to retrieve a catalog of every registered ML model in the workspace. This is your starting point for any investigation or prediction.
Can I check secrets using Baseten MCP? How does list_secrets work? +
list_secrets shows you which environment variables are available without ever exposing their values. It's the secure way to audit your credentials.
What is the difference between listing deployments and getting a deployment status with Baseten MCP? +
list_deployments gives you a list of all active instances for a model, while get_deployment provides the deep details (like replica counts or scaling rules) for one specific instance.
Do I need to use Baseten MCP for every prediction? +
No. But if your goal is to run a prediction against an ML model managed by Baseten, this MCP is the dedicated tool that handles the connection and execution flow.
What input format does the `predict` tool require for Baseten MCP? +
The predict tool requires payloads that strictly match your deployed model's expected shape. You must pass explicit tensor shapes or dictionaries directly to the GPU weights. This ensures the prediction executes cleanly and avoids formatting errors.
How does `list_deployments` help me check active inference boundaries for a model? +
list_deployments shows all currently running instances tied to a specific model ID. It lets you audit every active replica state and inferencing boundary without needing individual deployment IDs. This is crucial for verifying your scaling setup.
What specific details does the `get_model` tool provide for a Baseten model? +
get_model pulls comprehensive metadata about a single, specified Baseten model. You get its unique ID and configuration structure. This is necessary context before you can list or deploy instances using that model.
Does using `list_secrets` expose the actual values of my workspace keys? +
No, list_secrets only enumerates active environment secrets. It confirms which credentials are mapped and available in the isolated ecosystem without ever exposing their plaintext value. This maintains strict security integrity.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.