How to Use the DataRobot MCP in Pydantic AI

Q: How do Pydantic AI agents list DataRobot projects?

The agent calls listprojects as a standard tool. The framework parses the result into a type-safe object for your agent to use immediately.

Q: Can Pydantic AI handle DataRobot dataset lists?

Yes, listdatasets returns a collection that your agent can iterate over safely. The type-safe interface ensures your agent handles every field correctly.

Use DataRobot with Pydantic AI to enforce strict type-safety on every ML model and deployment metadata query.

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Connect DataRobot MCP to Pydantic AI

Create your Vinkius account to connect DataRobot to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup DataRobot with Pydantic AI

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Validate DataRobot models in Pydantic AI

The `list_models` tool returns data that your agent validates against Pydantic schemas. If the API structure shifts, your agent catches it before executing bad logic. You build reliable systems that stop on error. It creates a robust check for your ML registry operations.

Query deployments with Pydantic AI

Your agent calls `list_deployments` to fetch active model versions. Because you use Pydantic AI, every returned field is verified at runtime. You remove the risk of silent corruption in your agent's context. Your code knows exactly what it's looking at.

Fetch project data in Pydantic AI

Use `get_project` to grab configuration details for your ML pipeline. The framework ensures the output matches your expected data structure perfectly. You gain total control over your DataRobot interactions. Your agent acts only when the metadata passes all validation rules.

Setup guide

Set up DataRobot MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "datarobot-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to DataRobot tools.",
)

result = await agent.run("List recent DataRobot transactions")
print(result.output)

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Common questions about DataRobot MCP in Pydantic AI

It guarantees that the data your agent reads is valid. Every response is checked against your models, preventing runtime crashes from unexpected API changes.

The agent calls `list_projects` as a standard tool. The framework parses the result into a type-safe object for your agent to use immediately.

Your server connection is isolated within a V8 sandbox managed by Vinkius. Only authorized agent calls touch the DataRobot registry, and all connections are transient.

Yes, `list_datasets` returns a collection that your agent can iterate over safely. The type-safe interface ensures your agent handles every field correctly.

The framework triggers a validation error immediately. Your agent handles the exception rather than proceeding with corrupted information.

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