How to Use the World Bank Labor & Trade MCP in Pydantic AI

Q: Can World Bank Labor & Trade metrics be used with Pydantic AI?

Yes, and it's smart. You can pull getfdi (Foreign Direct Investment) data, and Pydantic validates that the net inflow percentage is present before your agent uses it in any calculation.

Q: Does World Bank Labor & Trade support comprehensive trade indicators via Pydantic AI?

It does. When you use getlabortradeindicator, the resulting data is immediately validated against your schema, ensuring that any custom or specialized indicator field is usable.

Q: What specific labor indicator codes are available via World Bank Labor & Trade with Pydantic AI?

The getlabortradeindicator tool accepts standardized World Bank codes. The resulting data, regardless of the code queried, is guaranteed to match your defined Python type structure.

Ensure data correctness when analyzing global labor and trade with Pydantic AI.

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Connect World Bank Labor & Trade MCP to Pydantic AI

Create your Vinkius account to connect World Bank Labor & Trade 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 Labor & Trade with Pydantic AI

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Get Export Data by Type

When you use `get_exports`, the MCP Server returns structured data on goods/services exports (% of GDP). Because your agent uses Pydantic, it validates that this export percentage field exists and is formatted correctly. If the World Bank API sends unexpected JSON fields, your agent fails loudly with a validation error. You don't risk silent corruption when analyzing global trade metrics.

Validate Labor Force Metrics

Call `get_labor_force` to get total labor force figures. Pydantic ensures the resulting structure contains exactly what you expect—a clean, validated count. This is crucial when building critical reports on employment levels. Your agent can trust this data because the framework guarantees the schema. You'll know immediately if a field is missing or formatted wrong.

Check Unemployment Rates Safely

The `get_unemployment_rate` tool provides the unemployment total as a percentage of labor force. Pydantic validates this rate against your expected model structure, making it reliable for financial modeling. This type safety prevents errors when comparing rates across different countries or time periods. You care about correctness more than speed here.

Setup guide

Set up World Bank Labor & Trade 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-labor-trade-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 Labor & Trade tools.",
)

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

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Common questions about World Bank Labor & Trade MCP in Pydantic AI

The server provides raw data, and your agent uses Pydantic to enforce strict schemas on every response. This means you get reliable, guaranteed-to-be-correct data structures for analysis.

Yes, and it's smart. You can pull `get_fdi` (Foreign Direct Investment) data, and Pydantic validates that the net inflow percentage is present before your agent uses it in any calculation.

The server deals with percentages of GDP, ratios, and total counts. Pydantic makes sure these numeric metrics are always correctly typed (e.g., float vs int) at runtime.

It does. When you use `get_labor_trade_indicator`, the resulting data is immediately validated against your schema, ensuring that any custom or specialized indicator field is usable.

The `get_labor_trade_indicator` tool accepts standardized World Bank codes. The resulting data, regardless of the code queried, is guaranteed to match your defined Python type structure.

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