How to Use the Tomorrow.io Plus MCP in LangChain

Q: How do I search for past Tomorrow.io Plus data using LangChain?

You can pull historical records by calling getrecenthistoryweather. This function retrieves weather history covering the last 24 hours, which your agent then incorporates into its reasoning chain.

Q: What is the best way to find active alerts with Tomorrow.io Plus and LangChain?

Use the listalerts tool. This function lists all current weather warnings based on locations and your specific insights. The output can then guide a subsequent API call, making your chain highly targeted.

Q: Can I check multiple location types with Tomorrow.io Plus and LangChain?

Yes. You have access to listlocations to see all predefined accounts areas. For more granular control over boundaries, the posttimelines tool accepts GeoJSON geometries.

Q: Does Tomorrow.io Plus support complex data inputs for LangChain?

Absolutely. The posttimelines function specifically handles complex location types like GeoJSON geometries, letting your agent process boundary-based forecasts.

Build multi-step weather reasoning pipelines with LangChain and the MCP Server.

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Connect Tomorrow.io Plus MCP to LangChain

Create your Vinkius account to connect Tomorrow.io Plus 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 Tomorrow.io Plus with LangChain

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Claude

Perplexity

Chaining Forecast Data in LangChain

Need to predict a full week's worth of conditions? Your agent can call `get_forecast_weather` first. It retrieves hourly or daily weather forecasts, giving the initial data point. The next step is easy: use that forecast output as input for a subsequent tool call, like checking specific precipitation intensity via `get_timelines`. LangChain handles this whole chain of reasoning.

Analyzing Real-Time Conditions with MCP Server

To ground your agent's current state, you can run a quick check using `get_realtime_weather`. This function pulls the exact current weather for any location. Later, if you need to compare that immediate data against what happened yesterday, call `get_recent_history_weather`. You build a complete narrative flow where the agent transitions from 'now' to 'then'.

Handling Complex GeoJSON Inputs

When dealing with complex areas that aren't simple points, you can use `post_timelines`. This tool accepts data for complicated location types like GeoJSON geometries. This process allows your multi-step LangChain agent to calculate weather patterns across an entire boundary. It's crucial when the scope of the forecast requires mapping beyond a single zip code.

Setup guide

Set up Tomorrow.io Plus 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 Tomorrow.io Plus 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({
    "tomorrowio-plus-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 Tomorrow.io Plus transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Tomorrow.io Plus MCP in LangChain

You can pull historical records by calling `get_recent_history_weather`. This function retrieves weather history covering the last 24 hours, which your agent then incorporates into its reasoning chain.

Use the `list_alerts` tool. This function lists all current weather warnings based on locations and your specific insights. The output can then guide a subsequent API call, making your chain highly targeted.

Yes. You have access to `list_locations` to see all predefined accounts areas. For more granular control over boundaries, the `post_timelines` tool accepts GeoJSON geometries.

Absolutely. The `post_timelines` function specifically handles complex location types like GeoJSON geometries, letting your agent process boundary-based forecasts.

This server touches structured weather data, including hourly and daily temperature and precipitation intensity records. All tool calls are processed through Vinkius's secure endpoint token.

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