How to Use the ERS USDA (Economic Research) MCP in Pydantic AI

Q: How do I use ERS USDA (Economic Research) with Pydantic AI?

Install the pydantic-ai-slimmcp package. Create an MCPToolset pointing to your Vinkius HTTP endpoint, then add it to your Agent's toolsets list.

Q: What happens if the USDA API changes its response format?

The agent fails loudly. If getarmsyears suddenly returns objects instead of integers, Pydantic catches the mismatch and stops execution rather than corrupting your agent's context.

Type-safe USDA farm data integration for your Pydantic AI agents.

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Connect ERS USDA (Economic Research) MCP to Pydantic AI

Create your Vinkius account to connect ERS USDA (Economic Research) 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 ERS USDA (Economic Research) with Pydantic AI

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Execute Type-Safe MCP Server Queries

The `get_arms_surveydata` tool requires strict parameter combinations, specifically a year plus a report or variable. Your Pydantic AI agent constructs these queries to pull exact financial records from the USDA. If the API returns a string where a float was expected, the framework throws a validation error immediately. You never get silent failures or hallucinated farm metrics injected into your downstream analysis.

Validate Farm Metadata

The `get_arms_farmtypes` tool returns structural classifications for agricultural operations. The agent uses this alongside `get_arms_states` to map out regional farm distributions. Every response passes through Pydantic models at runtime. When your agent requests state codes, the framework ensures the returned data strictly matches the expected schema before the LLM even sees it.

Audit Available Reports

The `get_arms_reports` tool lists the exact financial and production datasets available in the ARMS database. Your agent calls this to verify a report exists before attempting to query it. You combine this with `get_arms_categories` to build a verifiable data pipeline. The agent checks the categories, finds the relevant report, and requests the specific variables, with every step strictly typed.

Setup guide

Set up ERS USDA (Economic Research) 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": {
        "ers-usda-economic-research-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ERS USDA (Economic Research) tools.",
)

result = await agent.run("List recent ERS USDA (Economic Research) transactions")
print(result.output)

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Common questions about ERS USDA (Economic Research) MCP in Pydantic AI

Install the `pydantic-ai-slim[mcp]` package. Create an `MCPToolset` pointing to your Vinkius HTTP endpoint, then add it to your Agent's `toolsets` list.

Agricultural financial data requires precision. The framework guarantees that the structural classifications and debt metrics returned from the API match your expected Python types exactly.

The agent fails loudly. If `get_arms_years` suddenly returns objects instead of integers, Pydantic catches the mismatch and stops execution rather than corrupting your agent's context.

No. The MCP standard automatically provides the schemas for all seven tools. Pydantic AI reads these definitions dynamically and enforces them during execution.

Your agent processes USDA agricultural resource surveys and farm financial variables through a highly restricted pipeline. Vinkius handles the external connection via short-lived, ephemeral containers, ensuring no query parameters leak outside the execution window.

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