How to Use the World Bank Full Access MCP in Pydantic AI

Q: Can I use Pydantic AI to compare diverse World Bank Full Access metrics?

Yes. You can query for getpoverty (headcount ratio) and then get the getgdp total, knowing that both outputs conform strictly to your defined Python models.

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Connect World Bank Full Access MCP to Pydantic AI

Create your Vinkius account to connect World Bank Full Access 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 Full Access with Pydantic AI

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Perplexity

Retrieve and validate structured economic data.

The `get_exports` tool fetches export percentages, returning a clean, validated structure. You can then combine this output with the `get_fdi` (Foreign Direct Investment) data point to model trade balance sheets accurately. The Pydantic framework ensures these numbers are always correctly typed.

Build reliable demographic models using Pydantic AI.

When you call `get_literacy_rate` or check the `get_total_population`, Pydantic validates the schema. This means if an indicator returns garbage, your agent fails immediately with a clear error, preventing silent data corruption in your pipelines.

Access and verify global sustainability metrics.

Use `get_forest_area` for land coverage percentages. Pair that with `get_renewable_energy`, and the resulting dataset is guaranteed to match the defined Pydantic schema. Correctness first—that's what this setup delivers.

Setup guide

Set up World Bank Full Access 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-full-access-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 Full Access tools.",
)

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

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Common questions about World Bank Full Access MCP in Pydantic AI

Pydantic validates every single MCP response against a defined model at runtime. If the API returns unexpected fields or types, your agent doesn't process bad data; it throws a loud validation error.

Yes. You can query for `get_poverty` (headcount ratio) and then get the `get_gdp` total, knowing that both outputs conform strictly to your defined Python models.

The server touches a vast range: percentages (inflation rate), absolute values (GDP in US$), and ratios (Gini index). Your agent treats these diverse metrics as validated, structured fields.

It is. Because of the type-safety layer provided by Pydantic, you get predictable behavior. You know exactly what structure to expect from tools like `get_indicator_data` every single time.

This MCP Server focuses on aggregated economic and demographic indicators, including population statistics, which are returned as validated metrics like percentages or totals.

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