How to Use the World Bank Education & Health MCP in Pydantic AI

Q: How do I check educational metrics using World Bank Education & Health and Pydantic AI?

Use geteduhealthindicator. The agent automatically validates the output against a defined schema, guaranteeing that every piece of information you get is correctly typed before your code uses it.

Q: Can I get life expectancy using World Bank Education & Health and Pydantic AI?

Yes. You call getlifeexpectancy, but the crucial part is that Pydantic validates the result. This means you're protected against unexpected data formats, which saves major debugging time.

Use World Bank Education & Health metrics and ensure perfect output structure with Pydantic AI.

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Connect World Bank Education & Health MCP to Pydantic AI

Create your Vinkius account to connect World Bank Education & Health 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 Education & Health with Pydantic AI

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Get Structured Global Indicators

The `get_edu_health_indicator` tool retrieves any educational or health indicator, but the real value is in the guarantee. The agent validates this response against a Pydantic model, so you never get unexpected fields or bad data types. This means that whether you pull a literacy rate or an expenditure figure, your downstream code knows exactly what structure to expect. No silent corruption.

Reliably Compare Demographic Data

Pulling `get_life_expectancy` and `get_infant_mortality` needs consistency. Pydantic AI enforces that these two metrics arrive in a defined, predictable format. If the source data is malformed, your agent fails loudly—you know immediately something is wrong. This certainty lets you build critical production agents where correctness trumps speed.

Validate Socioeconomic Measurements

When checking `get_literacy_rate` and `get_health_expenditure`, the type-safe approach guarantees that the outputs are correct. You won't get a string when you expect a float, for example. This strict validation is perfect for agents building reports or databases where data integrity cannot be compromised.

Setup guide

Set up World Bank Education & Health 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-education-health-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 Education & Health tools.",
)

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

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Common questions about World Bank Education & Health MCP in Pydantic AI

Use `get_edu_health_indicator`. The agent automatically validates the output against a defined schema, guaranteeing that every piece of information you get is correctly typed before your code uses it.

Yes. You call `get_life_expectancy`, but the crucial part is that Pydantic validates the result. This means you're protected against unexpected data formats, which saves major debugging time.

The MCP Server exposes indicators, health spending, infant mortality rate, life expectancy at birth, and literacy rate. The Pydantic layer ensures that all these diverse global metrics adhere to a single, reliable Python data structure.

It manages it by validating each tool call's output individually. If one of the five tools—say, `get_infant_mortality`—returns bad data, your agent fails gracefully with a clear validation error instead of letting you process corrupted information.

The server touches aggregate global statistics like indicators and mortality rates. Pydantic AI ensures that even if these stats are complex, your agent treats them as strongly typed Python objects.

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