Weather MCP Server

Overview

This project implements a Model Context Protocol (MCP) server that exposes weather forecast tools, backed by the Open-Meteo API. It can be deployed as a Databricks App and integrated with Agent Bricks to answer natural-language weather questions.

Architecture

┌────────────────────────────────────────────┐
│  Weather MCP Server (Databricks App)      │
│  ┌──────────────────────────────────────┐ │
│  │  weather_mcp_server.py               │ │
│  │  - FastMCP with @mcp.tool decorators │ │
│  │  - get_current_weather()             │ │
│  │  - get_forecast()                    │ │
│  │  - predict_umbrella_needed()         │ │
│  └──────────────────────────────────────┘ │
│             ↓                              │
│  ┌──────────────────────────────────────┐ │
│  │  weather_broker.py                   │ │
│  │  - HTTP calls to Open-Meteo API      │ │
│  │  - Geocoding (city → lat/lon)        │ │
│  │  - Weather code decoding (WMO)       │ │
│  │  - Error handling                    │ │
│  └──────────────────────────────────────┘ │
└────────────────────────────────────────────┘
                   ↓ MCP protocol
┌────────────────────────────────────────────┐
│  Agent Bricks Agent                        │
│  - Uses weather tools via MCP              │
│  - Answers natural language questions      │
│  - Makes recommendations                   │
└────────────────────────────────────────────┘

Project Structure

weather_mcp_server/
├── weather_mcp_server.py   # FastMCP server with tool decorators
├── weather_broker.py        # API adapter (HTTP calls, parsing)
├── app.yaml                 # Databricks App configuration
├── requirements.txt         # Python dependencies
└── README.md                # This file

MCP Tools (3 Required)

1. get_current_weather(location: str)

Purpose: Fetch real-time weather conditions for any location.

Arguments:

• location (str): City name or "City, Country" format

Returns: JSON with temperature (C/F), conditions, humidity, wind speed, precipitation, cloud cover

Example:

get_current_weather("Chicago")
# Returns: {"location": {"name": "Chicago", "country": "United States"}, 
#           "current": {"temperature_c": 22.5, "conditions": "Partly cloudy", ...}}

---

2. get_forecast(location: str, days: int = 7)

Purpose: Multi-day weather forecast (1-16 days ahead).

Arguments:

• location (str): City name
• days (int): Number of forecast days (1-16, default 7)

Returns: JSON with daily high/low temps, precipitation chance/amount, conditions, wind speed

Example:

get_forecast("Seattle", 3)
# Returns: {"location": {...}, "forecast": [
#   {"date": "2026-08-09", "temp_max_c": 24.0, "precipitation_chance": 60, ...},
#   {...}, {...}
# ]}

---

3. predict_umbrella_needed(location: str, date: str = None, threshold_percent: int = 40)

Purpose: Smart recommendation - should you bring an umbrella?

Arguments:

• location (str): City name
• date (str, optional): Target date in "YYYY-MM-DD" format (default: tomorrow)
• threshold_percent (int, optional): Precipitation probability threshold (default: 40)

Decision Logic (NOT just a passthrough):

• Recommends umbrella if EITHER:
  1. Precipitation chance > threshold_percent (default 40%), OR
  2. Expected rainfall >= 2mm

Returns: JSON with recommendation, reasoning, forecast details, and decision rule explanation

Example:

predict_umbrella_needed("Portland", "2026-08-15")
# Returns: {
#   "recommendation": "Yes, bring an umbrella",
#   "reasoning": "High chance of rain (65% > 40% threshold) with significant rainfall...",
#   "forecast_details": {"precipitation_chance": 65, "precipitation_mm": 4.5, ...},
#   "decision_rule": "Umbrella recommended if: (precipitation_chance > 40%) OR (expected_rainfall >= 2mm)"
# }

---

Weather API Details

API Used: Open-Meteo
Authentication: None required (free tier, up to ~10,000 calls/day for non-commercial use)
Endpoints Used:

• Geocoding API: https://geocoding-api.open-meteo.com/v1/search
• Forecast API: https://api.open-meteo.com/v1/forecast

Why Open-Meteo?

• No signup or API key required
• Free and reliable
• Returns WMO weather codes (decoded to human-readable strings)
• Supports both current conditions and multi-day forecasts

---

Setup Instructions

Step 1: Deploy the MCP Server as a Databricks App

1. Navigate to Databricks Apps:

   • In your Databricks workspace, go to Compute → Apps

2. Create a new app:

   databricks apps create weather-mcp-server \
     --source-code-path /Workspace/Users/<your-email>/weather_mcp_server
   

3. Deploy the app:

   databricks apps deploy weather-mcp-server
   

4. Get the app URL:

   databricks apps get weather-mcp-server
   

   Note the url field - you'll need this for Agent Bricks registration.

Step 2: Register the MCP Server with Agent Bricks

1. Navigate to Agent Bricks:

   • In Databricks, go to Machine Learning → Agents

2. Create a new agent or edit an existing one

3. Add External Tool:

   • Click "Add Tool" → "External MCP Tool"
   • Tool URL: <your-app-url> (from Step 1)
   • Tool Type: MCP

4. Configure System Prompt:

   You are a weather assistant powered by real-time weather data.
   
   Available tools:
   - get_current_weather(location): Get current conditions
   - get_forecast(location, days): Get multi-day forecast (1-16 days)
   - predict_umbrella_needed(location, date, threshold_percent): Smart umbrella recommendation
   
   Guidelines:
   - Always call tools to get data - never guess or hallucinate weather information
   - If a location cannot be found, ask the user to clarify or provide a different location
   - For umbrella predictions, explain the reasoning based on the decision rule
   - Present temperatures in both Celsius and Fahrenheit
   - If an API call fails, inform the user clearly rather than making up data
   

5. Save and test!

Step 3: Test the Agent

Try these example queries:

1. Current conditions:

   • "What's the weather like in Chicago right now?"
   • "Tell me the current temperature in Tokyo"

2. Forecasts:

   • "Will it rain in Seattle this weekend?"
   • "What's the 5-day forecast for Austin?"

3. Recommendations:

   • "Should I bring an umbrella to Boston tomorrow?"
   • "Do I need a jacket in San Francisco on August 12th?"

---

Key Design Decisions

1. Separation of Concerns

• weather_broker.py: All HTTP calls, geocoding, error handling
• weather_mcp_server.py: Thin MCP tool wrappers, JSON serialization
• Benefit: MCP tools stay clean and testable; broker can be mocked

2. No Hardcoded Credentials

• Open-Meteo requires no API key
• If using a different API, follow this pattern:

  from databricks.sdk import WorkspaceClient
  
  def _get_api_key():
      w = WorkspaceClient()
      return w.secrets.get_secret(scope="weather", key="api_key").value
  

3. Error Handling

• Custom WeatherBrokerError exception
• All tools return JSON (never raise exceptions to MCP client)
• Clear error messages: {"error": "Location 'XYZ' not found"}

4. Prediction Tool Design

• NOT just a passthrough of raw forecast data
• Applies business logic: (precip_chance > threshold) OR (precip_mm >= 2mm)
• Explains reasoning in natural language
• Includes decision rule in output for transparency

---

Testing Locally (Optional)

You can test the MCP server locally before deploying:

# Install dependencies
pip install -r requirements.txt

# Run the server
python weather_mcp_server.py

# Server will start on http://localhost:8000
# Use MCP client or curl to test tools

---

Example Agent Interactions

Example 1: Current Weather

User: "What's the weather in London right now?"

Agent:
→ Calls get_current_weather("London")
← Returns: {"current": {"temperature_c": 18.5, "temperature_f": 65.3, "conditions": "Partly cloudy", ...}}
Response: "In London, it's currently 18.5°C (65.3°F) with partly cloudy skies. Humidity is at 72%, and winds are light at 12 km/h."

---

Example 2: Forecast Query

User: "Will it rain in Portland this week?"

Agent:
→ Calls get_forecast("Portland", 7)
← Returns: {"forecast": [{"date": "2026-08-09", "precipitation_chance": 65, ...}, ...]}
Response: "Looking at Portland's 7-day forecast, rain is likely on:

• Aug 9: 65% chance, 4.5mm expected (Moderate rain)
• Aug 11: 40% chance, 2.1mm expected (Light rain showers)

The rest of the week looks mostly dry!"

---

Example 3: Umbrella Recommendation

User: "Should I bring an umbrella to Austin tomorrow?"

Agent:
→ Calls predict_umbrella_needed("Austin", "tomorrow")
← Returns: {"recommendation": "No umbrella needed", "reasoning": "Low precipitation chance (15% <= 40% threshold) and minimal expected rainfall (0.2mm < 2mm)..."}
Response: "No umbrella needed for Austin tomorrow (Aug 9)! There's only a 15% chance of rain with minimal expected rainfall (0.2mm). Conditions will be mainly clear with highs of 34°C (93°F)."

---

Stretch Features (Not Implemented, Ideas for Extra Credit)

1. Severe weather alerts (using NWS API for US locations)
2. Historical weather lookup ("What was the weather in NYC last Christmas?")
3. Multi-city comparison ("Which is warmer this weekend, Miami or LA?")
4. Packing recommendations ("What should I pack for a trip to Iceland next week?")
5. Dashboard app showing recent agent queries and predictions

---

Troubleshooting

Problem: "Location 'XYZ' not found"
Solution: Try a more specific location (e.g., "Springfield, Illinois" instead of "Springfield")

Problem: MCP tools not showing up in Agent Bricks
Solution: Verify the app is deployed and the URL is correct. Check app logs: databricks apps logs weather-mcp-server

Problem: "Forecast API error: timeout"
Solution: Open-Meteo may be temporarily unavailable. Retry after a minute.

---