Weather (Open-Meteo) MCP Server for Pydantic AI 7 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Weather (Open-Meteo) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Weather (Open-Meteo) "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in Weather (Open-Meteo)?"
)
print(result.data)
asyncio.run(main())
* 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 Weather (Open-Meteo) MCP Server
Connect to Open-Meteo and empower your AI agent with high-precision meteorological data through natural conversation. This server provides comprehensive weather insights for any location on Earth without requiring complex API configurations.
Pydantic AI validates every Weather (Open-Meteo) tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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
- Current Conditions — Retrieve real-time temperature, humidity, wind speed, and precipitation for any city
- Precise Forecasts — Get 7-day daily forecasts or detailed 24-hour hourly breakdowns to plan your activities
- Air Quality — Monitor US AQI levels and dominant pollutants (PM2.5, PM10, Ozone) with health recommendations
- Weather Alerts — Receive critical notifications for severe weather conditions like storms, heavy snow, or high UV levels
- City Comparisons — Compare weather conditions between two different cities to help with travel or logistics planning
- Best Time Analysis — Find the optimal window for outdoor activities based on temperature and precipitation thresholds
- Global Coverage — Seamlessly geocode any city name into coordinates for instant weather retrieval
The Weather (Open-Meteo) MCP Server exposes 7 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 Weather (Open-Meteo) to Pydantic AI via MCP
Follow these steps to integrate the Weather (Open-Meteo) 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 7 tools from Weather (Open-Meteo) with type-safe schemas
Why Use Pydantic AI with the Weather (Open-Meteo) MCP Server
Pydantic AI provides unique advantages when paired with Weather (Open-Meteo) 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 Weather (Open-Meteo) integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Weather (Open-Meteo) connection logic from agent behavior for testable, maintainable code
Weather (Open-Meteo) + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Weather (Open-Meteo) MCP Server delivers measurable value.
Type-safe data pipelines: query Weather (Open-Meteo) with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Weather (Open-Meteo) tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Weather (Open-Meteo) and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Weather (Open-Meteo) responses and write comprehensive agent tests
Weather (Open-Meteo) MCP Tools for Pydantic AI (7)
These 7 tools become available when you connect Weather (Open-Meteo) to Pydantic AI via MCP:
weather.air_quality
Always display the health_recommendation from the result. AQI scale: 0-50=Good, 51-100=Moderate, 101-150=Unhealthy for Sensitive Groups, 151-200=Unhealthy, 201-300=Very Unhealthy, 301+=Hazardous. Get current air quality index (AQI), PM2.5, PM10, ozone, and NO2 for any city
weather.alerts
Alerts are computed from current conditions and the 7-day forecast. Returns an empty list if no significant conditions are detected. Get active weather alerts and advisories for any city, derived from forecast data
weather.best_time
This tool analyses hourly data for 72 hours and scores each 3-hour window using activity-specific criteria. Supports activities: general, hiking, beach, cycling, running, outdoor_work, photography, picnic. Always show the tip from the top-ranked window. Find the best time windows in the next 72 hours to do an outdoor activity in any city, with a comfort score
weather.compare
g. "which city has better weather?", "should I go to Lisbon or Madrid this weekend?", "compare weather in NYC, London, and Tokyo"). Accepts 2 to 5 cities. Computes a comfort score for each and highlights the best option. Compare current weather conditions across multiple cities side by side, with comfort scores
weather.current
Accept natural language city names (e.g. "São Paulo", "New York", "Paris, France"). Do NOT use for forecasts — use weather.forecast for that. Get current weather conditions for any city in the world
weather.forecast
Default to 7 days if the user does not specify. Max 14 days. For today's hour-by-hour breakdown, use weather.hourly instead. Get a multi-day weather forecast (1–14 days) for any city in the world
weather.hourly
Always covers the next 24 hours from now. For multi-day overview, use weather.forecast instead. Get an hour-by-hour weather forecast for the next 24 hours for any city
Example Prompts for Weather (Open-Meteo) in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Weather (Open-Meteo) immediately.
"What's the weather like in Tokyo right now?"
"Give me the 7-day forecast for London."
"Compare the weather between New York and Miami."
Troubleshooting Weather (Open-Meteo) MCP Server with Pydantic AI
Common issues when connecting Weather (Open-Meteo) to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiWeather (Open-Meteo) + Pydantic AI FAQ
Common questions about integrating Weather (Open-Meteo) MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Weather (Open-Meteo) with your favorite client
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Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Weather (Open-Meteo) to Pydantic AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.