Can the AI agent run a prediction directly against my hosted model?

Yes. By pushing a correctly formatted JSON payload to the 'predict' tool, the agent securely triggers inference on the GPU instances, returning the exact calculated response data transparently to your editor context.

Is my workspace and environmental secret data kept safe?

Baseten secret fetching natively obscures variable values. When you use 'list_secrets', the agent simply evaluates the key names and identifiers existing across your environment to verify configurations without exposing plaintext passwords.

How do I check auto-scaling configurations for an explicitly deployed model?

You can examine exactly how instances are managed by using 'get_deployment'. Tell the agent to target an active deployment ID and it maps the scaling limits, replica status, and container bounds out-of-the-box.

Baseten MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: Baseten
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Baseten through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Baseten "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Baseten?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Baseten MCP Server

Connect your Baseten account to any AI agent and track, deploy, and execute your machine learning models through natural conversation.

Pydantic AI validates every Baseten tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

O que você pode fazer

Model Management — List managed models, fetch configurations, and understand active routing boundaries
Serverless Deployments — Inspect exact replica states, autoscaling configurations, and deployment versions
Inference Execution — Run direct predictions (predict) pushing tensor payloads or JSON directly to GPU weights
Workspace Secrets — Enumerate active environment secrets securely mapped inside the isolated orchestration ecosystem

Como funciona

1. Subscribe to this server
2. Enter your Baseten API Key
3. Gain complete ML-Ops control over your active inference nodes using Claude, Cursor, or your preferred agent

Scale unified AI infrastructure without bouncing between terminal windows. Your agent becomes a capable Machine Learning Operator tracking your GPU lifecycle.

Para quem é?

ML Engineers — execute test payloads to deployments instantaneously without spinning up local Python notebooks
DevOps/SREs — audit running deployment resources and verify replica states reliably from your core IDE
AI Researchers — inspect version schemas and manage inference pipeline architectures quickly

The Baseten MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Baseten to Pydantic AI via MCP

Follow these steps to integrate the Baseten MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Baseten with type-safe schemas

Why Use Pydantic AI with the Baseten MCP Server

Pydantic AI provides unique advantages when paired with Baseten through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Baseten integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Baseten connection logic from agent behavior for testable, maintainable code

Baseten + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Baseten MCP Server delivers measurable value.

Type-safe data pipelines: query Baseten with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Baseten tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Baseten and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Baseten responses and write comprehensive agent tests

Baseten MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Baseten to Pydantic AI via MCP:

get_deployment

Get explicit details of a running deployment

get_model

Get a specific Baseten model

list_deployments

List active inferences bounds matching a specific model

list_models

List Baseten managed models

list_secrets

List securely managed workspace secrets without showing values

predict

Formulate the explicit tensor shapes or dictionaries strictly matching the deployed instance. Invoke a serverless model inference prediction

Example Prompts for Baseten in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Baseten immediately.

"List standard machine learning models we currently host on Baseten."

"Run a prediction against the Sentiment model ID 12345 using this text input: 'The new feature completely broke my workflow.'"

"Check if our Baseten project has a secret scoped as 'OPENAI_API_KEY_FALLBACK'."

Troubleshooting Baseten MCP Server with Pydantic AI

Common issues when connecting Baseten to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Baseten + Pydantic AI FAQ

Common questions about integrating Baseten MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Baseten MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.