How to Use the Data.gov Catalog MCP in Pydantic AI

Q: How do I hook up the Data.gov Catalog MCP Server to Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your Vinkius HTTP URL, then pass it to the toolsets array when building your Agent.

Q: Should I use MCPServerHTTP for Data.gov Catalog in Pydantic AI?

No, that class is deprecated. Use the unified MCPToolset approach which supports both Streamable HTTP and SSE transports out of the box.

Strongly typed access to federal datasets using Pydantic AI and runtime validation.

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Connect Data.gov Catalog MCP to Pydantic AI

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

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Perplexity

Type-Safe Ingestion Audits

Government APIs change without warning. When your agent calls `get_harvest_record`, Pydantic AI validates the incoming metadata against your defined schemas before the LLM even sees it. If the `get_harvest_record_transformed` payload drops a required DCAT-US field, the framework fails loudly. You get a strict validation error instead of a hallucinated response ruining your downstream logic.

Strict Federal Queries via MCP Server

Searching thousands of datasets requires exact parameters. The agent triggers `search_datasets` using keywords and spatial filters, knowing the response will match your exact expectations. Pulling the full directory via `get_organizations` works the same way. The framework guarantees the returned list of publishing entities is formatted correctly, letting you focus on application logic rather than parsing messy JSON.

Map Processing with Pydantic AI

Handling geographic data in Python can get sloppy. Running `search_locations` grabs the autocomplete ID, which the agent then feeds into `get_location_geometry`. The resulting GeoJSON boundary gets validated at runtime. You can swap between Anthropic or local models without losing any type checking on your spatial workflows.

Setup guide

Set up Data.gov Catalog 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-catalog-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 Catalog tools.",
)

result = await agent.run("List recent Data.gov Catalog 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 Catalog MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your Vinkius HTTP URL, then pass it to the `toolsets` array when building your Agent.

No, that class is deprecated. Use the unified `MCPToolset` approach which supports both Streamable HTTP and SSE transports out of the box.

It catches bad data the second it arrives. If the federal API returns a string instead of an integer for dataset counts in `get_keywords`, the framework throws a validation error immediately.

The framework is completely model-agnostic. You can query federal datasets using OpenAI, Gemini, or a local Llama model while keeping the same exact tool definitions.

The tools only touch public dataset metadata and federal organization lists. Every request passes through a Vinkius zero-trust sandbox that isolates the memory space and terminates instantly after execution.

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