How to Use the Feature Scaler Engine MCP in Pydantic AI

Use Feature Scaler Engine with Pydantic AI for type-safe, validated data normalization.

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Connect Feature Scaler Engine MCP to Pydantic AI

Create your Vinkius account to connect Feature Scaler Engine 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 Feature Scaler Engine with Pydantic AI

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Type-safe scaling for Pydantic AI

The `scale_features` tool returns results that map directly to your Pydantic models. If the math produces an unexpected type, the agent fails immediately. This prevents silent corruption in your data pipeline. You get absolute confidence that your agent is working with valid, scaled figures.

Strict runtime validation

Pydantic AI validates every response from this MCP server. You define the schema, and the engine ensures the data conforms to your expectations. You don't have to worry about hallucinated fields or malformed numbers. The `scale_features` tool provides the exact data structure your agent needs.

Flexible transport for agents

We support SSE and HTTP transports, so you can connect to the server however your agent architecture requires. It's built for stability in production. The `scale_features` tool runs outside your process, keeping your agent logic lean. You just define the toolset and let the framework handle the rest.

Setup guide

Set up Feature Scaler Engine 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": {
        "feature-scaler-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Feature Scaler Engine transactions")
print(result.output)

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Common questions about Feature Scaler Engine MCP in Pydantic AI

You define a Pydantic model for the expected numeric output. The framework validates the server response against this model at runtime.

The agent will raise a validation error immediately. This prevents your model from training on or reasoning with bad data.

Yes, the toolset is model-agnostic. Whether you use a local model or a cloud provider, the scaling logic remains consistent.

Yes, it supports SSE and HTTP. You configure your MCPToolset to match your preferred transport protocol.

We manage your data in an ephemeral, single-use sandbox. Your numeric inputs are never persisted, ensuring your privacy remains intact.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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