How to Use the EPA AirNow MCP in Pydantic AI

Q: How do I add EPA AirNow to a Pydantic AI agent?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your Vinkius HTTP URL and include it in the toolsets array when defining your agent.

Type-safe environmental data for Pydantic AI agents.

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Connect EPA AirNow MCP to Pydantic AI

Create your Vinkius account to connect EPA AirNow 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 EPA AirNow with Pydantic AI

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Claude

Perplexity

Connect the EPA AirNow MCP Server

Silent failures in environmental data can cause massive logistical headaches. You use `get_current_aqi_by_zip` to pull the current index, knowing the response must match your exact schema. Pydantic AI validates every metric at runtime. If the EPA API changes its payload structure or returns a string instead of an integer, your agent fails loudly with a validation error rather than hallucinating a safe air quality score.

Ingest strict forecast predictions

You need to know if tomorrow's air will be hazardous. The `get_forecast_aqi_by_zip` tool fetches the upcoming predictions from the federal network. Because Pydantic AI is model-agnostic, you can swap between Claude, OpenAI, or local models. The agent logic remains identical while it processes these forecasts and decides whether to trigger automated alerts.

Pinpoint local sensor data

Broad regional averages often hide localized smoke plumes. The `get_current_aqi_by_latlon` tool forces the agent to look at the nearest physical monitor to your target coordinates. You set this up fast using the unified MCPToolset approach. The server runs externally on Vinkius, handling the connection while your Python code focuses strictly on agent behavior and data validation.

Setup guide

Set up EPA AirNow 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": {
        "epa-airnow-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent EPA AirNow transactions")
print(result.output)

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Common questions about EPA AirNow MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Initialize an MCPToolset with your Vinkius HTTP URL and include it in the toolsets array when defining your agent.

Yes. If the agent calls the coordinate tool and receives an incomplete JSON response, the framework throws a strict validation error immediately.

Absolutely. The framework separates the MCP Server logic from the LLM provider. You can run a local model while fetching real-world data from the Vinkius endpoints.

Pydantic AI supports Streamable HTTP and SSE transports. You just provide the Vinkius URL and the framework handles the underlying connection.

The tool strictly handles 5-digit ZIP codes and float-based coordinates. Vinkius executes these requests inside an isolated V8 sandbox, destroying the environment entirely once the API returns the air quality metrics.

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