Data.gov Catalog MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Get Harvest Record, Get Harvest Record Raw, Get Harvest Record Transformed, and more

Name: Data.gov Catalog
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Data.gov Catalog through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Data.gov Catalog MCP Server for Pydantic AI is a standout in the Data Analytics category — giving your AI agent 8 tools to work with, ready to go from day one.

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Data.gov Catalog "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Data.gov Catalog?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Data.gov Catalog MCP Server

Connect to the Data.gov Catalog to explore the comprehensive repository of US Government open data. This MCP server allows AI agents to discover datasets from agencies like NASA, NOAA, and the Census Bureau through natural language.

Pydantic AI validates every Data.gov Catalog tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Dataset Discovery — Search the entire catalog using keywords, organization filters, and advanced sorting via search_datasets.
Spatial Analysis — Find datasets by geographic location using GeoJSON boundaries and spatial filters with search_locations and get_location_geometry.
Organization Insights — List all publishing organizations and filter results by specific agency slugs using get_organizations.
Metadata Inspection — Retrieve detailed harvest records, including raw and transformed DCAT-US payloads with get_harvest_record_raw and get_harvest_record_transformed.
Keyword Trends — Analyze commonly used keywords and their dataset counts to identify data trends using get_keywords.

The Data.gov Catalog MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 8 Data.gov Catalog tools available for Pydantic AI

When Pydantic AI connects to Data.gov Catalog through Vinkius, your AI agent gets direct access to every tool listed below — spanning open-data, federal-data, dataset-discovery, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

get

Get harvest record on Data.gov Catalog

Retrieve metadata about how a dataset was ingested

get

Get harvest record raw on Data.gov Catalog

Retrieve original unmodified source payload for a harvest record

get

Get harvest record transformed on Data.gov Catalog

Retrieve transformed DCAT-US payload for a harvest record

get

Get keywords on Data.gov Catalog

Retrieve commonly used keywords and their dataset counts

get

Get location geometry on Data.gov Catalog

Retrieve the GeoJSON boundary for a specific location ID

get

Get organizations on Data.gov Catalog

Retrieve the complete list of publishing organizations

Search datasets on Data.gov Catalog

Search the catalog using keywords, filters, and sorting

Search locations on Data.gov Catalog

Autocomplete search for location names to use with spatial filtering

Connect Data.gov Catalog to Pydantic AI via MCP

Follow these steps to wire Data.gov Catalog into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Data.gov Catalog with type-safe schemas

Why Use Pydantic AI with the Data.gov Catalog MCP Server

Pydantic AI provides unique advantages when paired with Data.gov Catalog through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Data.gov Catalog integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Data.gov Catalog connection logic from agent behavior for testable, maintainable code

Data.gov Catalog + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Data.gov Catalog MCP Server delivers measurable value.

Type-safe data pipelines: query Data.gov Catalog with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Data.gov Catalog tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Data.gov Catalog and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Data.gov Catalog responses and write comprehensive agent tests

Example Prompts for Data.gov Catalog in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Data.gov Catalog immediately.

"Search for NASA datasets related to climate change."

"List all government organizations that publish data here."

"Get the GeoJSON boundary for 'Los Angeles' to filter my search."

Troubleshooting Data.gov Catalog MCP Server with Pydantic AI

Common issues when connecting Data.gov Catalog to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Data.gov Catalog + Pydantic AI FAQ

Common questions about integrating Data.gov Catalog MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Data.gov Catalog MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.