How to Use the World Bank Countries MCP in Pydantic AI

Ensure data correctness for World Bank Countries with Pydantic AI's type safety.

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Connect World Bank Countries MCP to Pydantic AI

Create your Vinkius account to connect World Bank Countries 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 World Bank Countries with Pydantic AI

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Type-Safe Access to World Bank Countries Data

The `list_countries` tool provides a raw list of all World Bank countries. With Pydantic AI, you wrap the response in a model, guaranteeing that every field—every ISO code and name—is exactly what you expect. If the API sends unexpected data, your agent fails loudly with an error. No silent corruption.

Validate Geographic Metadata using MCP Server

Need to filter countries by region? The `search_regions` tool does the heavy lifting on World Bank geography. By validating this result against a Pydantic model, you ensure your agent only processes structured, correct regional data. It prevents runtime errors caused by unexpected field names or missing values.

Structured Checks for Income Levels with Pydantic AI

The `search_income_levels` tool gives you the classification (e.g., HIC, LIC). Using Pydantic guarantees that this data adheres to a strict schema. You can build complex business logic around these validated types. This focus on correctness means your agent's output is reliable, which is critical for production systems.

Setup guide

Set up World Bank Countries 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": {
        "world-bank-countries-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent World Bank Countries transactions")
print(result.output)

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Common questions about World Bank Countries MCP in Pydantic AI

Every response from the MCP Server is validated against your defined Pydantic models at runtime. If the data doesn't fit the structure, the agent throws a clear validation error—no guesswork involved.

Yes. You pass the toolset and define your expected output schema around `search_regions`. This keeps your agent's data flow predictable, regardless of the source.

This server handles country ISO codes, global geographic regions, and income/lending classifications. These structured strings are perfect for defining strict Pydantic schemas.

It does. You define a model that accepts inputs from multiple tools—say, combining `list_countries` and `search_income_levels`—and it validates the combined result.

The type-safe validation is the security layer. It ensures that even if external systems provide slightly corrupted metadata, your agent fails cleanly instead of generating incorrect outputs.

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