The Tomorrow.io Model Context Protocol (MCP) server enables AI agents and MCP-compatible clients to access Tomorrow.io weather data through standardized tools.

This hosted MCP endpoint allows you to query real-time, forecast, historical, and event-based weather data using your Tomorrow.io API key.

Overview

With the Tomorrow.io MCP server, you can:

Access weather data through MCP-compatible AI tools (e.g., Cursor, Claude, Windsurf)
Retrieve real-time, forecast, and historical weather
Analyze weather events and route-based risks
Integrate weather intelligence directly into LLM workflows

Endpoint

Use the following MCP endpoint:

https://api.tomorrow.io/v4/tomorrow-weather/mcp

Authentication

Authenticate using your Tomorrow.io API key via request headers:

 X-Api-Key: <YOUR_TOMORROW_API_KEY

How It Works

The Tomorrow.io MCP server exposes weather capabilities as tools that can be called by MCP-compatible clients (such as Cursor, Claude, or Windsurf).

A typical flow:

Your MCP client connects to the Tomorrow.io MCP endpoint
The client discovers available tools (e.g., get_forecast_timeline)
An LLM or agent decides which tool to call based on user intent
The tool executes and returns structured weather data
The client uses the response to generate an answer or take action

Quick Start

Example MCP Configuration

Most MCP clients support a similar configuration format:

{
  "mcpServers": {
    "tomorrow-weather-mcp": {
      "type": "http",
      "url": "https://api.tomorrow.io/v4/tomorrow-weather/mcp",
      "headers": {
        "X-Api-Key": "${TOMORROW_API_KEY}"
      }
    }
  }
}

Client Integrations

Cursor

Add to your .mcp.json:

{
  "mcpServers": {
    "tomorrow-weather-mcp": {
      "type": "http",
      "url": "https://api.tomorrow.io/v4/tomorrow-weather/mcp",
      "headers": {
        "X-Api-Key": "${TOMORROW_API_KEY}"
      }
    }
  }
}

Claude Desktop (via mcp-remote)

Claude Desktop requires a bridge to connect to HTTP MCP servers:

{
  "mcpServers": {
    "tomorrow-weather-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://api.tomorrow.io/v4/tomorrow-weather/mcp",
        "--header",
        "X-Api-Key: ${TOMORROW_API_KEY}"
      ],
      "env": {
        "TOMORROW_API_KEY": "REPLACE_WITH_YOUR_API_KEY"
      }
    }
  }
}

Other MCP Clients (Cline, Roo, Windsurf)

Use the standard HTTP MCP configuration:

{
  "mcpServers": {
    "tomorrow-weather-mcp": {
      "type": "http",
      "url": "https://api.tomorrow.io/v4/tomorrow-weather/mcp",
      "headers": {
        "X-Api-Key": "${TOMORROW_API_KEY}"
      }
    }
  }
}

Available Tools

The MCP server exposes the following tools:

Tool	Description
`get_realtime_weather`	Current weather conditions
`get_forecast_timeline`	Forecast over a time range
`get_historical_weather`	Past observed weather
`get_climate_normals`	Long-term climate averages
`get_weather_events`	Condition-based event timelines
`get_route_weather_events`	Weather events along routes

Example: Natural Language to Tool Calls

User request:

"Will it rain in New York in the next 6 hours?"

Tool used:

get_forecast_timeline

User request:

"Was it hotter than 30°C last week?"

Tool used:

get_historical_weather

User request:

"What is the temperature right now in New York?"

Tool used:

get_realtime_weather

Tool Reference

get_realtime_weather

Retrieve current weather conditions for a location.

Use cases

“What is the weather right now?”
Current conditions dashboards

Inputs

location (required): "lat,lon" or place name
units (optional): metric (default) or imperial

Returns

Observation timestamp
Temperature, humidity, wind, precipitation, visibility, pressure, UV index, and more

Example

Call:

{
  "tool": "get_realtime_weather",
  "arguments": {
    "location": "40.7128,-74.0060",
    "units": "metric"
  }
}

Response:

{
  "data": {
    "time": "2026-04-28T10:00:00Z",
    "values": {
      "temperature": 18.5,
      "temperatureApparent": 18.2,
      "humidity": 63,
      "windSpeed": 4.2,
      "windDirection": 210,
      "windGust": 6.8,
      "precipitationIntensity": 0,
      "visibility": 16,
      "cloudCover": 35,
      "pressureSurfaceLevel": 1015,
      "uvIndex": 5
    }
  },
  "location": {
    "lat": 40.7128,
    "lon": -74.0060,
    "name": "New York, USA"
  }
}

get_forecast_timeline

Retrieve forecast data across a time range.

Use cases

Planning upcoming weather
Forecast-based decision making

Inputs

location (required)
fields (required): weather parameters
startTime, endTime (optional)
timesteps (optional): 1h, 1d, 5m, 15m, 30m
units
timezone (optional)

Returns

Time-series forecast intervals
Field values per interval

Example

Call:

{
  "tool": "get_forecast_timeline",
  "arguments": {
    "location": "New York, NY",
    "fields": ["temperature", "precipitationProbability"],
    "timesteps": ["1h"],
    "startTime": "now",
    "endTime": "nowPlus6h",
    "units": "metric"
  }
}

Response:

{
  "timelines": [
    {
      "timestep": "1h",
      "intervals": [
        {
          "startTime": "2026-04-28T10:00:00Z",
          "values": {
            "temperature": 18.5,
            "precipitationProbability": 10
          }
        },
        {
          "startTime": "2026-04-28T11:00:00Z",
          "values": {
            "temperature": 19.2,
            "precipitationProbability": 15
          }
        }
      ]
    }
  ]
}