How do I find my MeteoSource API Key?

Sign up for a free account at [**meteosource.com**](https://www.meteosource.com/), and you will find your API Key in your dashboard. Copy and paste it below.

Does it support location search by name?

Yes. Use the `search_weather_places` tool providing the city or place name. Your agent will return the unique `place_id` required for forecasts.

Are hourly forecasts included?

Yes. The `get_point_forecast` tool retrieves comprehensive weather metadata including current conditions, daily summaries, and hourly breakdowns.

MeteoSource MCP Server for LangChain 5 tools — connect in under 2 minutes

Name: MeteoSource
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect MeteoSource through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "meteosource": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using MeteoSource, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 MeteoSource MCP Server

Empower your AI agent to orchestrate your entire meteorological research and weather auditing workflow with MeteoSource, the comprehensive source for hyper-local weather data. By connecting the MeteoSource API to your agent, you transform complex forecast searches into a natural conversation. Your agent can instantly search for monitored places, audit daily and hourly forecasts, and retrieve timezone metadata without you ever touching a weather portal. Whether you are planning outdoor events or conducting regional climate audits, your agent acts as a real-time meteorological consultant, ensuring your data is always precise and localized.

LangChain's ecosystem of 500+ components combines seamlessly with MeteoSource through native MCP adapters. Connect 5 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Place Auditing — Search for thousands of global locations and retrieve high-resolution place IDs and geographic metadata.
Forecast Oversight — Audit comprehensive point forecasts, including current conditions, daily summaries, and hourly breakdowns.
Geographic Discovery — Find the nearest monitored place by latitude and longitude to maintain strict organizational control over local data.
Temporal Intelligence — Query timezone information for specific places to assist in time-sensitive logistics and event planning.
Operational Monitoring — Check API status to ensure your meteorological research workflow is always operational.

The MeteoSource MCP Server exposes 5 tools through the Vinkius. Connect it to LangChain 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 MeteoSource to LangChain via MCP

Follow these steps to integrate the MeteoSource MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 5 tools from MeteoSource via MCP

Why Use LangChain with the MeteoSource MCP Server

LangChain provides unique advantages when paired with MeteoSource through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine MeteoSource MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across MeteoSource queries for multi-turn workflows

MeteoSource + LangChain Use Cases

Practical scenarios where LangChain combined with the MeteoSource MCP Server delivers measurable value.

RAG with live data: combine MeteoSource tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query MeteoSource, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain MeteoSource tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every MeteoSource tool call, measure latency, and optimize your agent's performance

MeteoSource MCP Tools for LangChain (5)

These 5 tools become available when you connect MeteoSource to LangChain via MCP:

check_api_status

Check if the MeteoSource service is operational

get_nearest_weather_place

Find the nearest monitored place by latitude and longitude

get_place_timezone

Get timezone information for a specific place_id

get_point_forecast

Get weather forecast for a specific place_id

search_weather_places

Search for a place by name to get its place_id for forecasts

Example Prompts for MeteoSource in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with MeteoSource immediately.

"Get weather forecast for 'London' using MeteoSource."

"Search for weather station near latitude 48.8566 and longitude 2.3522."

"What is the timezone for place 'tokyo'?"

Troubleshooting MeteoSource MCP Server with LangChain

Common issues when connecting MeteoSource to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

MeteoSource + LangChain FAQ

Common questions about integrating MeteoSource MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.