Open-Meteo MCP Server for Pydantic AI 5 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect 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 Open-Meteo "
"(5 tools)."
),
)
result = await agent.run(
"What tools are available in 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 Open-Meteo MCP Server
Connect to Open-Meteo and access global weather forecasts through natural conversation — no API key needed.
Pydantic AI validates every Open-Meteo tool response against typed schemas, catching data inconsistencies at build time. Connect 5 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 Weather — Get real-time temperature, humidity, wind, precipitation and conditions
- 7-Day Forecast — Hourly and daily forecasts up to 16 days ahead with 50+ weather variables
- Historical Weather — Access archived weather data going back to 1940 for any location
- Air Quality — Get PM2.5, PM10, NO2, O3, SO2, CO and UV index forecasts
- Geocoding — Find coordinates for any city or place name
- Elevation — Get elevation data for any coordinates
The Open-Meteo MCP Server exposes 5 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 to Pydantic AI via MCP
Follow these steps to integrate the 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 5 tools from Open-Meteo with type-safe schemas
Why Use Pydantic AI with the Open-Meteo MCP Server
Pydantic AI provides unique advantages when paired with 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 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 Open-Meteo connection logic from agent behavior for testable, maintainable code
Open-Meteo + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Open-Meteo MCP Server delivers measurable value.
Type-safe data pipelines: query Open-Meteo with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple 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 Open-Meteo and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Open-Meteo responses and write comprehensive agent tests
Open-Meteo MCP Tools for Pydantic AI (5)
These 5 tools become available when you connect Open-Meteo to Pydantic AI via MCP:
get_air_quality
5, PM10, nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide, dust, pollen and UV index. Requires latitude and longitude. Returns hourly data for up to 7 days. Common variables: pm2_5, pm10, nitrogen_dioxide, ozone, sulphur_dioxide, carbon_monoxide, dust, uv_index, alder_pollen, grass_pollen. Get air quality forecast for a location
get_elevation
Useful for hiking, aviation and geographic research. Get elevation for coordinates
get_forecast
Requires latitude and longitude. Supports hourly, daily and current weather variables. Common variables: temperature_2m, relative_humidity_2m, precipitation, rain, snowfall, wind_speed_10m, wind_direction_10m, wind_gusts_10m, weather_code, cloud_cover, pressure_msl, uv_index, visibility, apparent_temperature, dew_point_2m, sunshine_duration. Set past_days to include historical data (0-92 days). Set forecast_days for forecast length (0-16 days, default 7). Timezone defaults to GMT; use "auto" for local timezone. Get weather forecast for a location
get_geocoding
Useful for finding coordinates to use with weather tools. Returns up to 10 results by default. Find coordinates for a place name
get_historical_weather
Requires latitude, longitude, start date and end date (YYYY-MM-DD format). Supports the same hourly variables as the forecast API. Historical data goes back to 1940 for most locations. Use get_geocoding to find coordinates for a city name. Get historical weather data for a location
Example Prompts for Open-Meteo in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Open-Meteo immediately.
"What's the weather forecast for São Paulo this week?"
"What was the temperature in Tokyo on July 15, 2024?"
"What's the air quality in Beijing right now?"
Troubleshooting Open-Meteo MCP Server with Pydantic AI
Common issues when connecting Open-Meteo to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiOpen-Meteo + Pydantic AI FAQ
Common questions about integrating 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 Open-Meteo with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
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 Open-Meteo to Pydantic AI
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.