How to Use the USGS Earthquakes MCP in Pydantic AI

Q: How do I find recent seismic activity using USGS Earthquakes and Pydantic AI?

You use getsignificant30days. The response is automatically validated, giving you confidence that the data structure for all significant global earthquakes from the last 30 days is correct.

Q: What are the parameters needed when querying USGS Earthquakes with Pydantic AI?

The queryearthquakes tool accepts start time, end time, minimum magnitude, and geographic boundaries. Since every response is validated, you know exactly how to structure your input.

Q: If I need to count quakes matching my criteria using USGS Earthquakes and Pydantic AI, which function should I use?

Use countearthquakes. The resulting total count is run through a schema validator, ensuring the output is always a correct integer type.

Q: Can Pydantic AI handle complex historical data retrieval from USGS Earthquakes?

Yes. By using queryearthquakes, your agent can pull massive datasets defined by specific time ranges, and the Pydantic validation ensures those records are perfectly typed.

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Connect USGS Earthquakes MCP to Pydantic AI

Create your Vinkius account to connect USGS Earthquakes 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 USGS Earthquakes with Pydantic AI

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Search global quakes using Pydantic AI

Use the `query_earthquakes` tool to search USGS real-time seismic data. You pass parameters like start/end times, minimum magnitude, and geographic boundaries. Every field returned is validated against a defined schema. This guarantees that if the API returns unexpected JSON or corrupted fields, your agent fails immediately with a clear validation error—no silent data corruption.

Monitor significant quakes over 30 days with Pydantic AI

Call `get_significant_30_days` for a list of highly significant global earthquakes from the last month. Because this is type-safe, you know exactly what fields to expect—no surprises. This is crucial when your agent needs to pass data to another service that expects a very specific payload structure.

Get a total count of seismic events with Pydantic AI

The `count_earthquakes` tool calculates the total number of earthquakes matching criteria. You define the parameters, and it returns the count, which is immediately validated as an integer. This guarantees that your agent always receives predictable data types for simple metrics.

Setup guide

Set up USGS Earthquakes 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": {
        "usgs-earthquakes-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent USGS Earthquakes transactions")
print(result.output)

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Common questions about USGS Earthquakes MCP in Pydantic AI

You use `get_significant_30_days`. The response is automatically validated, giving you confidence that the data structure for all significant global earthquakes from the last 30 days is correct.

The `query_earthquakes` tool accepts start time, end time, minimum magnitude, and geographic boundaries. Since every response is validated, you know exactly how to structure your input.

Use `count_earthquakes`. The resulting total count is run through a schema validator, ensuring the output is always a correct integer type.

Yes. By using `query_earthquakes`, your agent can pull massive datasets defined by specific time ranges, and the Pydantic validation ensures those records are perfectly typed.

This server touches real-time seismic event records from the USGS network. The specific data is structured earthquake event information, including magnitude, location coordinates, and time stamps.

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