How to Use the HealthData.gov (HHS Open Data) MCP in Pydantic AI

Q: How do I set up the HealthData.gov (HHS Open Data) server with Pydantic AI?

First, run pip install "pydantic-ai-slimmcp". Then, initialize the connection using MCPToolset with your Vinkius HTTP endpoint and pass it to your Agent's toolsets parameter.

Query public health datasets with absolute type safety using Pydantic AI and validated Socrata schemas.

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Connect HealthData.gov (HHS Open Data) MCP to Pydantic AI

Create your Vinkius account to connect HealthData.gov (HHS Open Data) 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 HealthData.gov (HHS Open Data) with Pydantic AI

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Discover HHS datasets safely with Pydantic AI

The `get_catalog` tool retrieves metadata and dataset identifiers directly from the HealthData.gov registry. Your agent can search for specific health topics and receive structured results that conform to strict Python types. Because Pydantic AI enforces strict data validation, any catalog metadata returned by this MCP Server is parsed and validated at runtime. This prevents silent failures if the API schema changes unexpectedly.

Run validated Socrata queries using SoQL

The `query_dataset` tool allows your agent to execute complex SoQL queries to pull targeted health data. You can filter by state, limit records, or select specific columns to fetch exactly what your application requires. Every dataset response is validated against your Pydantic models before your agent processes it. If a dataset returns unexpected null values or incorrect data types, the system fails loudly, protecting your production database.

Build resilient, model-agnostic health data pipelines

Both `get_catalog` and `query_dataset` work natively across different LLM providers using this MCP integration. You can switch from OpenAI to Anthropic or Gemini without changing your data-fetching logic. Using the unified toolset interface, your agent can discover datasets, construct queries, and validate the results in a single, type-safe workflow. This minimizes runtime errors and keeps your data pipelines highly reliable.

Setup guide

Set up HealthData.gov (HHS Open Data) 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": {
        "healthdatagov-hhs-open-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent HealthData.gov (HHS Open Data) 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 HealthData.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 HealthData.gov (HHS Open Data) MCP in Pydantic AI

First, run `pip install "pydantic-ai-slim[mcp]"`. Then, initialize the connection using `MCPToolset` with your Vinkius HTTP endpoint and pass it to your Agent's `toolsets` parameter.

If the API response doesn't match your defined schema, Pydantic AI will raise a validation error immediately. This prevents your agent from processing corrupt or incomplete public health records.

Yes. Since the framework is model-agnostic, you can connect this server to agents running on local models or commercial APIs. The tool definitions remain identical regardless of the underlying LLM.

Your agent calls the catalog tool to query metadata across thousands of HHS datasets. The returned list of dataset IDs is parsed into structured objects, making it easy for the agent to select the correct target.

This server only accesses public datasets on HealthData.gov, so it never touches private patient files, medical histories, or HIPAA-regulated medical records. All connections are routed through Vinkius's secure MCP sandbox, keeping your API endpoints safe.

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