How to Use the INMET (Apitempo - Meteorologia) MCP in LangChain

Q: How do I connect the INMET (Apitempo - Meteorologia) MCP server to my LangChain agent?

Install langchain-mcp-adapters via pip. Initialize the client with the Vinkius transport URL, then pull the tools using client.gettools() to pass them directly to your agent constructor.

Run multi-step weather analysis chains using INMET (Apitempo - Meteorologia) meteorological tools directly inside LangChain.

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Connect INMET (Apitempo - Meteorologia) MCP to LangChain

Create your Vinkius account to connect INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) with LangChain

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Perplexity

Chain live station telemetry to your agent

The `get_station_data_hourly` tool pulls raw climate telemetry from specific Brazilian stations. This feeds directly into the next link of your chain, letting you pass wind speed or temperature readings straight to an agricultural calculation model without manual parsing. LangSmith traces every step of this data flow. You see the exact payload size and execution latency for `get_station_data_daily` as your pipeline processes historical weather patterns.

Feed GOES-16 imagery to LangChain pipelines

The `get_satellite_images` tool fetches the latest satellite visuals from INMET. Your agent feeds these image URLs directly into vision-capable models to detect cloud cover or storm fronts over specific regions. This setup avoids hardcoded API keys. You configure the server once, and your chain uses the active session to pull real-time visual data when regional temperatures cross a defined threshold.

Build ReAct workflows with the INMET MCP Server

The `get_forecast_by_city` tool retrieves municipal weather forecasts for your agent. The agent evaluates the forecast, checks it against historical baselines, and determines if it needs to query `list_stations` for deeper local data. You manage these multi-step decisions using standard LangGraph structures. The agent handles the tool-calling loop automatically, resolving dependencies between regional data and municipal forecasts in real time.

Setup guide

Set up INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) 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({
    "inmet-apitempo-meteorologia-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 INMET (Apitempo - Meteorologia) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about INMET (Apitempo - Meteorologia) MCP in LangChain

Install `langchain-mcp-adapters` via pip. Initialize the client with the Vinkius transport URL, then pull the tools using `client.get_tools()` to pass them directly to your agent constructor.

Yes, LangSmith tracks every tool execution automatically. You will see the exact milliseconds it takes to fetch hourly data from Brazilian weather stations.

The agent uses ReAct loops to decide which tools to call. For example, it might call `get_all_forecasts` first, analyze the results, and then query `get_station_data_daily` for specific cities that show anomalous readings.

Your graph handles the error like any standard tool exception. You can configure fallback paths or retry mechanisms directly inside your LangGraph state definition to keep the run alive.

No, your physical coordinates are never sent. The server only processes public parameters like station IDs and city names, running inside an isolated sandbox to keep your infrastructure details private.

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