How to Use the Nationalize MCP in Pydantic AI

Q: How do I connect Nationalize to Pydantic AI?

Install the package with pip install "pydantic-ai-slimmcp" and use the unified MCPToolset class. Pass the Vinkius HTTP endpoint to the toolset and include it in your agent's toolsets list.

Q: How does Pydantic AI validate Nationalize outputs?

The framework checks the raw JSON response from predictnationality against internal Pydantic models. Any missing fields or invalid types trigger a runtime validation error, stopping bad data in its tracks.

Validate name-to-nationality predictions at runtime with Pydantic AI type-safe schemas.

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Connect Nationalize MCP to Pydantic AI

Create your Vinkius account to connect Nationalize 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 Nationalize with Pydantic AI

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Strict type validation for `predict_nationality`

The `predict_nationality` tool estimates the country of origin for any given surname. This MCP Server delivers structured JSON payloads directly to your Pydantic AI agent. Surnames yield the most reliable predictions from the underlying Nationalize.io API. The framework validates the returned country codes and probabilities against strict Python type schemas before your code runs.

Type-safe Pydantic AI tool integration

You connect the MCP server by importing `MCPToolset` from the `pydantic-ai-slim[mcp]` package. Pass the Vinkius HTTP endpoint directly to your agent's `toolsets` parameter. If the API returns unexpected data formats, the agent raises a validation error immediately. This prevents silent failures and corrupt data from propagating through your database.

Model-agnostic regional classification

This setup works with any LLM provider supported by Pydantic AI, including Anthropic, OpenAI, or local models. The agent uses the `predict_nationality` tool to determine geographic context. Your model receives clean, validated Python objects instead of raw string outputs. This structured output makes it easy to write reliable routing logic based on the predicted country of origin.

Setup guide

Set up Nationalize 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": {
        "nationalize-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Nationalize MCP in Pydantic AI

Install the package with `pip install "pydantic-ai-slim[mcp]"` and use the unified `MCPToolset` class. Pass the Vinkius HTTP endpoint to the toolset and include it in your agent's `toolsets` list.

The framework checks the raw JSON response from `predict_nationality` against internal Pydantic models. Any missing fields or invalid types trigger a runtime validation error, stopping bad data in its tracks.

This combination gives you structured, type-safe demographic data without writing custom MCP clients. Your agent can confidently process names knowing the schema is validated at the boundary.

Yes, the MCP Server runs in a secure Vinkius environment. Your Pydantic AI client connects to this hosted instance using Streamable HTTP or SSE transports, so you do not have to manage local server processes.

The name strings are sent directly to the Nationalize API to generate the prediction. Vinkius processes these requests in memory inside an ephemeral V8 sandbox, meaning no personal identifiers are stored or logged permanently.

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