How to Use the Weather (Open-Meteo) MCP in LangChain

Q: Can LangChain handle hourly weather data for multiple days?

Yep. While weather.hourly covers 24 hours, you can chain calls or use weather.forecast which supports up to 14 days, giving your agent a much longer view.

Build complex, multi-step reasoning pipelines with LangChain and the Weather (Open-Meteo) MCP Server.

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Connect Weather (Open-Meteo) MCP to LangChain

Create your Vinkius account to connect Weather (Open-Meteo) to LangChain 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 Weather (Open-Meteo) with LangChain

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Compare Weather Across Multiple Cities with LangChain

Need to know if you should go to Lisbon or Madrid? Your agent can compare multiple locations instantly. It runs `weather.compare` and returns a single comfort score for each city, letting the user pick the best option. This tool accepts anywhere from 2 to 5 cities, making it perfect for itinerary planning where the output of one comparison feeds directly into the next decision point in your chain.

Find Optimal Activity Times with LangChain and MCP Server

Don't just get a forecast; find the best window. The `weather.best_time` tool analyzes 72 hours of hourly data, scoring each three-hour block for activities like cycling or running. It outputs a tip based on the top score. This is great for creating complex chains: first, call `weather.best_time`; then, pass that result to another action agent that books the tickets.

Get Real-Time Conditions and Air Quality with LangChain

When you need a quick snapshot of what's happening right now—not just a forecast. Use `weather.current` for immediate conditions in any city, plus get the detailed air quality index (AQI) using `weather.air_quality`. The output always includes the health recommendation. This combination lets your agent perform quick checks and then use that data to decide if further actions are needed, like calling `weather.alerts`.

Setup guide

Set up Weather (Open-Meteo) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Weather (Open-Meteo) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "weather-open-meteo-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Weather (Open-Meteo) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Weather (Open-Meteo) MCP in LangChain

You chain the tools together. For instance, your agent can first call `weather.compare` to pick a city, and then immediately feed that chosen city into `weather.forecast` to get the full multi-day plan.

Yep. While `weather.hourly` covers 24 hours, you can chain calls or use `weather.forecast` which supports up to 14 days, giving your agent a much longer view.

It provides actionable data points that guide the chain. If the initial check using `weather.air_quality` shows 'Hazardous,' your agent knows to skip planning outdoor activities and go straight to recommending an indoor alternative.

The MCP Server supports various client types, so whether you use the standard library or a custom integration, the tool definitions remain consistent for your agent.

It handles detailed weather conditions and air quality metrics, including PM2.5 levels, ozone concentration, and specific health recommendations based on the AQI scale.

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