How to Use the Tomorrow.io Plus MCP in Pydantic AI

Q: How can I list and validate active alerts with Tomorrow.io Plus and Pydantic AI?

The listalerts tool fetches warnings. Your agent then passes this raw result through Pydantic validation. This ensures that the alert data—including severity levels and affected zones—matches your required model.

Q: Does Pydantic AI guarantee accurate historical weather records?

Yes. When using getrecenthistoryweather, every returned record is validated against your schema. You get a guaranteed data type for temperature, precipitationIntensity, and other metrics.

Q: What insights can I validate with Tomorrow.io Plus and Pydantic AI?

You first use listinsights to define the category. Then, when fetching event details via listevents, Pydantic ensures that every returned attribute—like date or location type—conforms precisely to your expected data model.

Ensure correct weather data structure using Pydantic AI with Tomorrow.io Plus.

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Connect Tomorrow.io Plus MCP to Pydantic AI

Create your Vinkius account to connect Tomorrow.io Plus 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 Tomorrow.io Plus with Pydantic AI

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Validate Real-Time Weather Data via MCP Server

When the agent uses `get_realtime_weather`, the response is immediately validated against your defined Pydantic model. This means if the API sends a field that shouldn't be there, the agent fails loudly, telling you exactly what broke. No silent data corruption. Your focus stays on correctness. The tool fetches current conditions, and Pydantic makes sure that data structure is perfect before your code runs.

Structure Forecasts Using Pydantic AI

The `get_forecast_weather` tool retrieves hourly or daily forecasts. By piping this through Pydantic, you guarantee the output fields—like temperature and wind speed—are always present and typed correctly. This eliminates guesswork from your agent. You get a guaranteed data shape, which is critical when building reliable, production-grade applications.

Handle Complex Locations with Pydantic AI

Use `post_timelines` to retrieve weather for complex shapes like GeoJSON geometries. The tool handles the geometry input, but Pydantic validates the returned data fields against your schema. This is how you build trust in your agent's output. You aren’t just getting weather; you’re getting perfectly structured, validated weather data.

Setup guide

Set up Tomorrow.io Plus 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": {
        "tomorrowio-plus-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Tomorrow.io Plus transactions")
print(result.output)

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Common questions about Tomorrow.io Plus MCP in Pydantic AI

The `list_alerts` tool fetches warnings. Your agent then passes this raw result through Pydantic validation. This ensures that the alert data—including severity levels and affected zones—matches your required model.

Yes. When using `get_recent_history_weather`, every returned record is validated against your schema. You get a guaranteed data type for temperature, precipitationIntensity, and other metrics.

You first use `list_insights` to define the category. Then, when fetching event details via `list_events`, Pydantic ensures that every returned attribute—like date or location type—conforms precisely to your expected data model.

The agent fails loudly. If the API returns unexpected data from `get_forecast_weather`, Pydantic catches it immediately with a validation error, stopping bad data from corrupting your application logic.

This MCP Server handles weather data: real-time conditions, historical records, and forecasts. The Pydantic validation layer adds an extra level of structure and control over how that specific environmental data is consumed.

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