How to Use the Data.gov MCP in Pydantic AI

Q: Can I use Data.gov metadata in my Pydantic AI models?

You can map the output of getdataset directly to your internal data classes. It ensures that metadata dates, tags, and license info are always in the correct format.

Strictly type-checked access to Data.gov for Pydantic AI. Ensure every federal data point matches your schema.

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Connect Data.gov MCP to Pydantic AI

Create your Vinkius account to connect Data.gov 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 Data.gov with Pydantic AI

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Type-safe Data.gov tool execution

Every response from the `get_dataset` tool is validated against your Pydantic models at runtime. If the API structure changes, the agent raises an error instead of passing bad data. This ensures your agent logic is built on a stable foundation. You define the shape of the data, and the server validates the incoming payload before your agent touches it.

Search and filter with runtime validation

Use `search_datasets` to query the federal catalog and receive results that conform to your defined types. Your agent avoids hallucinating fields because the validator rejects non-conforming data. It handles the search filters and metadata parsing, ensuring the output is always clean. You spend less time debugging data issues and more time building agent logic.

Navigate federal tags and organizations

The `list_tags` and `list_organizations` tools provide structured lists of the entire Data.gov directory. Pydantic AI validates these lists as they arrive. You can trust that the tag names and organization metadata are correctly formatted. It makes it easy to build reliable, type-aware agents that interact with federal open data.

Setup guide

Set up Data.gov 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": {
        "datagov-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Data.gov transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Data.gov. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Data.gov MCP in Pydantic AI

You install the MCP package and pass an MCPToolset to your Agent. The framework handles the validation of every tool response against your models automatically.

Yes. The framework checks the incoming data against your Pydantic schemas immediately. If the server returns something unexpected, the agent fails safely.

You can map the output of `get_dataset` directly to your internal data classes. It ensures that metadata dates, tags, and license info are always in the correct format.

Your agent will catch the validation error through Pydantic AI. This prevents silent data corruption by forcing a clear failure when the schema mismatches.

This server only retrieves public, non-sensitive government datasets. It has no access to your local environment or private Pydantic models, maintaining a strict boundary around your infrastructure.

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