How far back does the historical data go?

All the way to **January 1, 1940** — that's 84 years of continuous, hourly global weather data powered by ERA5 reanalysis from ECMWF.

Open-Meteo Historical Weather MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: Open-Meteo Historical Weather
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

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

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Vinkius supports streamable HTTP and SSE.

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 Open-Meteo Historical Weather "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Open-Meteo Historical Weather?"
    )
    print(result.data)

asyncio.run(main())

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About Open-Meteo Historical Weather MCP Server

Access 84 years of continuous weather records from 1940 to today for any location on Earth.

Pydantic AI validates every Open-Meteo Historical Weather tool response against typed schemas, catching data inconsistencies at build time. Connect 3 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

Historical Hourly — Temperature, humidity, precipitation, snowfall, weather codes, and wind for any past date range
Historical Daily — Max/min temperatures, precipitation totals, sunshine duration, and dominant wind patterns
Temperature Trends — Dedicated tool for long-term climate trend analysis with apparent temperature data

The Open-Meteo Historical Weather MCP Server exposes 3 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Open-Meteo Historical Weather to Pydantic AI via MCP

Follow these steps to integrate the Open-Meteo Historical Weather MCP Server with Pydantic AI.

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 3 tools from Open-Meteo Historical Weather with type-safe schemas

Why Use Pydantic AI with the Open-Meteo Historical Weather MCP Server

Pydantic AI provides unique advantages when paired with Open-Meteo Historical Weather 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 Open-Meteo Historical Weather integration code

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

Dependency injection system cleanly separates your Open-Meteo Historical Weather connection logic from agent behavior for testable, maintainable code

Open-Meteo Historical Weather + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Open-Meteo Historical Weather MCP Server delivers measurable value.

Type-safe data pipelines: query Open-Meteo Historical Weather with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Open-Meteo Historical Weather tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Open-Meteo Historical Weather and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Open-Meteo Historical Weather responses and write comprehensive agent tests

Open-Meteo Historical Weather MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect Open-Meteo Historical Weather to Pydantic AI via MCP:

get_historical_daily

Get historical daily weather aggregates

get_historical_temperature

Includes hourly temperature, apparent temperature, and dewpoint. Get historical temperature trends for climate analysis

get_historical_weather

Provide latitude, longitude, start_date and end_date in YYYY-MM-DD format. Covers 84 years of global data. Get historical weather for any date range (1940–present)

Example Prompts for Open-Meteo Historical Weather in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Open-Meteo Historical Weather immediately.

"What was the weather in London on D-Day, June 6, 1944?"

"Compare average temperatures in São Paulo between 1950 and 2020"

"How much rain fell in Mumbai during the 2005 flood?"

Troubleshooting Open-Meteo Historical Weather MCP Server with Pydantic AI

Common issues when connecting Open-Meteo Historical Weather to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Open-Meteo Historical Weather + Pydantic AI FAQ

Common questions about integrating Open-Meteo Historical Weather 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 Open-Meteo Historical Weather MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.