How to Use the USITC DataWeb (International Trade Commission) MCP in Pydantic AI

Q: What specific trade statistics can I query using the Pydantic AI framework?

You use querytradedata to pull US international trade statistics. You can focus on imports, exports, and detailed trade balances across various classification systems like NAICS or SITC.

Q: How does Pydantic AI handle data discovery for the USITC DataWeb (International Trade Commission)?

You start by calling listmetadatatables to survey available datasets. Then, use listmetadatafields to get a complete, validated list of all fields within any selected table.

Q: Can I validate my country code inputs using Pydantic AI?

Yes. You call listmetadatavalues specifying a field name to get an exhaustive list of all valid values, which is crucial for accurate international trade reporting.

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Connect USITC DataWeb (International Trade Commission) MCP to Pydantic AI

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Setup USITC DataWeb (International Trade Commission) with Pydantic AI

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Querying USITC Trade Data with Pydantic AI

Call `query_trade_data` to fetch US international trade statistics, filtering by HS, SITC, or NAICS codes. Because the MCP response is validated against your Pydantic models, you get predictable data—no silent failures. This guarantees that when your agent processes imports or exports, the resulting structure matches exactly what your code expects.

Schema Discovery for Pydantic AI

Need to know the underlying schema? First run `list_metadata_tables` and then use `list_metadata_fields`. This gives you a complete, typed definition of every data field in the USITC DataWeb (International Trade Commission) tables. If the API returns unexpected fields, your agent fails loudly with a validation error—you know immediately if something is wrong.

Validating Inputs for Pydantic AI

Use `list_metadata_values` to confirm valid data inputs. This tool takes a field name and returns all accepted values, like country codes or classifications. This pre-validation step ensures that the data you send into your agent is clean, verifiable, and ready for production code.

Setup guide

Set up USITC DataWeb (International Trade Commission) 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": {
        "usitc-dataweb-international-trade-commission-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent USITC DataWeb (International Trade Commission) transactions")
print(result.output)

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Common questions about USITC DataWeb (International Trade Commission) MCP in Pydantic AI

The core benefit is type safety. Every result from the MCP Server runs through Pydantic validation models. If the data format changes unexpectedly, your agent fails with a clear error, preventing corrupted or hallucinated fields.

You use `query_trade_data` to pull US international trade statistics. You can focus on imports, exports, and detailed trade balances across various classification systems like NAICS or SITC.

You start by calling `list_metadata_tables` to survey available datasets. Then, use `list_metadata_fields` to get a complete, validated list of all fields within any selected table.

This MCP Server touches trade statistics and classification codes. The agent's execution is constrained by the structured output validation, providing clear boundaries around the USITC DataWeb (International Trade Commission) data.

Yes. You call `list_metadata_values` specifying a field name to get an exhaustive list of all valid values, which is crucial for accurate international trade reporting.

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