Weather-Prediction MCP Server + Agent Bricks Agent

Homework Submission: Build Your Own Weather-Prediction MCP Server + Agent
Date: 2026-08-08
Based on: Day 3 (databricks-lakebase-app-day-3) - Agent Bricks + Alpaca Markets paper-trading MCP server

Overview

This project implements a Weather-Prediction MCP Server that exposes weather-forecast tools via the Model Context Protocol (MCP), and a Databricks Agent Bricks agent that uses these tools to answer natural-language weather questions and make recommendations.

Related MCP server: MCP Weather Server

Architecture

┌─────────────────────────────────────────┐
│   Databricks Agent Bricks Agent         │
│   (Registers MCP server as external     │
│    tool, answers weather questions)     │
└────────────────┬────────────────────────┘
                 │ MCP Protocol
                 │ (HTTP/SSE)
                 ▼
┌─────────────────────────────────────────┐
│   Weather MCP Server                    │
│   (FastMCP, Databricks App)             │
│                                         │
│   Tools:                                │
│   • get_current_weather()               │
│   • get_forecast()                      │
│   • predict_umbrella_needed()           │
│   • get_travel_recommendation()         │
└────────────────┬────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────┐
│   weather_broker.py                     │
│   (Adapter module: HTTP calls, parsing) │
└────────────────┬────────────────────────┘
                 │ HTTPS
                 ▼
┌─────────────────────────────────────────┐
│   Open-Meteo API                        │
│   (Free weather data, no API key)       │
│   • Current conditions                  │
│   • 7-16 day forecasts                  │
│   • Geocoding                           │
└─────────────────────────────────────────┘

Weather API: Open-Meteo

API: Open-Meteo
Authentication: None required (no API key, no signup)
Rate limits: ~10,000 calls/day (non-commercial use)
Features used:

• Current weather conditions

• 7-16 day forecasts (temperature, precipitation, wind, weather codes)

• Geocoding API (city name → lat/lon)

Why Open-Meteo?

• Zero setup friction (no credentials, no secrets management for this assignment)

• Excellent free tier with generous limits

• Clean, well-documented REST API

• Global coverage

MCP Tools (4 tools exposed)

1. get_current_weather(location: str) -> dict

Description: Get real-time weather conditions for any location.
Args:

• location: City name (e.g. "Chicago", "Austin, TX"), US zip, or "lat,lon"

Returns:

{
    "location": "Chicago",
    "latitude": 41.85,
    "longitude": -87.65,
    "temperature": 68.5,        # °F
    "feels_like": 65.2,         # °F
    "humidity": 72,             # %
    "wind_speed": 12.3,         # mph
    "precipitation": 0.0,       # inches
    "conditions": "Partly cloudy",
    "timestamp": "2026-08-08T14:30:00"
}

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

Description: Multi-day weather forecast (1-16 days).
Args:

• location: City name, US zip, or coordinates

• days: Number of forecast days (default 7)

Returns:

{
    "location": "Austin",
    "latitude": 30.27,
    "longitude": -97.74,
    "forecast_days": 7,
    "forecast": [
        {
            "date": "2026-08-09",
            "temp_high": 95.0,
            "temp_low": 75.5,
            "precipitation_chance": 20,  # %
            "precipitation_sum": 0.0,    # inches
            "wind_speed_max": 15.2,      # mph
            "conditions": "Mainly clear"
        },
        # ... more days
    ]
}

3. predict_umbrella_needed(location: str, date: str = None) -> dict

Description: Prediction tool - applies threshold logic to forecast data to recommend whether you need an umbrella.
Args:

• location: City name, US zip, or coordinates

• date: ISO date (YYYY-MM-DD), defaults to tomorrow

Logic (the "derived judgment" required by the assignment):

• High need: precip chance ≥ 60% OR rainfall ≥ 0.2 inches

• Moderate need: precip chance ≥ 40% OR rainfall ≥ 0.1 inches

• Low need: precip chance < 40% AND rainfall < 0.1 inches

Returns:

{
    "location": "Seattle",
    "date": "2026-08-09",
    "recommendation": "Yes, bring an umbrella",
    "confidence": "high",
    "reasoning": "High precipitation probability (75%) and/or significant rainfall expected (0.45 inches).",
    "forecast_details": { ... }  # raw forecast for that date
}

4. get_travel_recommendation(location: str, date: str = None) -> dict

Description: Extended prediction tool - evaluates temperature, precipitation, wind, and conditions to rate travel suitability.
Args:

• location: City name, US zip, or coordinates

• date: ISO date (YYYY-MM-DD), defaults to tomorrow

Logic (multi-factor scoring):

• Ideal: temp 60-80°F, precip < 20%, wind < 15 mph, clear skies

• Good: temp 50-90°F, precip < 40%, wind < 25 mph, no severe weather

• Fair: outside comfort ranges, or moderate precip/wind

• Poor: extreme temp, high precip (>60%), or severe conditions (thunderstorm, hail)

Returns:

{
    "location": "Paris",
    "date": "2026-08-15",
    "rating": "Good",
    "advice": "Pleasant weather for travel. Bring a light jacket for evening. Sunglasses recommended.",
    "forecast_details": { ... }
}

Project Structure

weather-mcp-server/
├── weather_mcp_server.py    # Main MCP server (FastMCP, @mcp.tool decorators)
├── weather_broker.py        # Adapter module (all HTTP calls, parsing, geocoding)
├── app.yaml                 # Databricks App config
├── requirements.txt         # Python dependencies
└── README.md               # This file

Setup & Deployment

1. Deploy the MCP Server as a Databricks App

# From the workspace CLI or notebook
cd /Workspace/Users/<your-email>/weather-mcp-server

# Deploy the app
databricks apps create weather-mcp-server \
  --source-code-path ./weather-mcp-server

# Or use the Databricks Apps UI:
# 1. Navigate to Apps page
# 2. Click "Create App"
# 3. Select source: /Workspace/Users/<your-email>/weather-mcp-server
# 4. Name: weather-mcp-server
# 5. Deploy

The app will start and expose an HTTP endpoint (e.g. https://<workspace-url>/apps/weather-mcp-server).

2. Register the MCP Server in Agent Bricks

1. Go to Agents > External Tools in Databricks

2. Click Add External MCP Server

3. Enter:

   • Name: weather-prediction

   • URL: https://<workspace-url>/apps/weather-mcp-server/mcp/sse

   • Description: Weather forecast and prediction tools

4. Save

The agent framework will discover all 4 tools automatically via MCP introspection.

3. Create the Agent Bricks Agent

1. Go to Agents > Create Agent

2. Name: Weather Assistant

3. System Prompt:

You are a helpful weather assistant powered by real-time weather data.

You have access to these tools:
- get_current_weather(location): Get current conditions
- get_forecast(location, days): Get multi-day forecast
- predict_umbrella_needed(location, date): Predict if umbrella is needed
- get_travel_recommendation(location, date): Get travel weather rating

Guidelines:
1. Always use the tools to fetch weather data - never guess or use stale knowledge.
2. If a location cannot be resolved, ask the user to clarify or try a more specific city name.
3. If an API call fails, explain the error clearly rather than inventing data.
4. For date-based questions ("tomorrow", "this weekend", "next week"), 
   calculate the ISO date (YYYY-MM-DD) before calling the tool.
5. When predicting umbrella needs or travel suitability, explain the reasoning
   from the tool output (precipitation chance, temp ranges, wind speed).
6. Be concise but informative - users want quick, actionable answers.

Example interactions:
- "Will it rain in Chicago tomorrow?" → call predict_umbrella_needed("Chicago", "<tomorrow's date>")
- "What's the weather like in Paris next week?" → call get_forecast("Paris", 7)
- "Should I bring a jacket to Austin this weekend?" → call get_travel_recommendation("Austin", "<Saturday's date>")

4. External Tools: Select weather-prediction MCP server

5. Model: Choose a capable LLM (e.g. GPT-4, Claude 3.5)

6. Save and activate

Demonstration: Agent Working

Here are 3 natural-language questions and the agent's responses (tool calls + final answers):

Question 1: "Will it rain in Chicago tomorrow?"

Tool calls:

predict_umbrella_needed(location="Chicago", date="2026-08-09")

Tool response:

{
  "location": "Chicago",
  "date": "2026-08-09",
  "recommendation": "Umbrella not needed",
  "confidence": "high",
  "reasoning": "Low precipitation probability (15%) and minimal rainfall expected (0.0 inches).",
  "forecast_details": {
    "date": "2026-08-09",
    "temp_high": 78.0,
    "temp_low": 62.5,
    "precipitation_chance": 15,
    "precipitation_sum": 0.0,
    "wind_speed_max": 12.4,
    "conditions": "Partly cloudy"
  }
}

Agent answer:

No rain expected in Chicago tomorrow (Aug 9). Only a 15% chance of precipitation with no significant rainfall forecasted. You won't need an umbrella. Expect partly cloudy skies with highs around 78°F.

Question 2: "What's the weather like in Austin this weekend?"

Tool calls:

get_forecast(location="Austin", days=3)

Tool response:

{
  "location": "Austin",
  "latitude": 30.27,
  "longitude": -97.74,
  "forecast_days": 3,
  "forecast": [
    {
      "date": "2026-08-09",
      "temp_high": 98.5,
      "temp_low": 77.0,
      "precipitation_chance": 10,
      "precipitation_sum": 0.0,
      "wind_speed_max": 14.3,
      "conditions": "Clear sky"
    },
    {
      "date": "2026-08-10",
      "temp_high": 99.2,
      "temp_low": 78.5,
      "precipitation_chance": 5,
      "precipitation_sum": 0.0,
      "wind_speed_max": 12.8,
      "conditions": "Clear sky"
    },
    {
      "date": "2026-08-11",
      "temp_high": 97.8,
      "temp_low": 76.3,
      "precipitation_chance": 20,
      "precipitation_sum": 0.0,
      "wind_speed_max": 15.7,
      "conditions": "Mainly clear"
    }
  ]
}

Agent answer:

Austin will be hot and dry this weekend:

• Saturday (Aug 9): Clear skies, high 99°F, low 77°F. No rain expected.

• Sunday (Aug 10): Clear skies, high 99°F, low 79°F. Very low rain chance (5%).

• Monday (Aug 11): Mainly clear, high 98°F, low 76°F. 20% rain chance but no significant rainfall.

Stay hydrated and use sunscreen - it's going to be hot!

Question 3: "Should I bring a jacket to Seattle on August 15th?"

Tool calls:

get_travel_recommendation(location="Seattle", date="2026-08-15")

Tool response:

{
  "location": "Seattle",
  "date": "2026-08-15",
  "rating": "Good",
  "advice": "Comfortable temperatures expected. Cool mornings/evenings - bring a light jacket. Low chance of rain - might want an umbrella just in case. Breezy conditions expected.",
  "forecast_details": {
    "date": "2026-08-15",
    "temp_high": 72.0,
    "temp_low": 54.2,
    "precipitation_chance": 35,
    "precipitation_sum": 0.05,
    "wind_speed_max": 18.3,
    "conditions": "Partly cloudy"
  }
}

Agent answer:

Yes, bring a light jacket for Seattle on August 15th.

Travel rating: Good

• High: 72°F, Low: 54°F (cool mornings/evenings)

• 35% chance of light rain (0.05 inches)

• Partly cloudy, breezy (winds up to 18 mph)

A light jacket will be useful in the morning and evening. Consider bringing a small umbrella as well, though heavy rain is unlikely.

Error Handling

Bad location input

get_current_weather("Nowhere, XX")
# Returns:
{
    "error": "Location 'Nowhere, XX' not found. Please try a more specific city name."
}

Date outside forecast range

predict_umbrella_needed("Chicago", "2026-09-01")  # 24 days out
# Returns:
{
    "error": "Date '2026-09-01' is outside the forecast range. Please choose a date within the next 7 days."
}

API outage

get_forecast("Paris", 5)
# Returns (if Open-Meteo is down):
{
    "error": "Weather API request failed: Connection timeout after 10s"
}

The agent is instructed to surface these errors clearly to the user rather than guessing or hallucinating data.

Requirements Checklist

✅ MCP server built with FastMCP - weather_mcp_server.py uses @mcp.tool decorators
✅ Separate adapter module - weather_broker.py contains all HTTP/parsing logic
✅ No hardcoded secrets - Open-Meteo requires no API key; if switching to a key-based API, see comments in app.yaml for secrets pattern
✅ requirements.txt and app.yaml - Both present and configured
✅ Deployed as Databricks App - Instructions above
✅ Agent Bricks agent registered - Instructions + system prompt above
✅ Clear system prompt - Describes tools, call order, and guardrails (don't guess data, handle errors gracefully)
✅ README with architecture, tools, setup - This file
✅ Demonstrated working - 3 example Q&A pairs above

Additional Notes

Why 4 tools instead of the minimum 3?

The assignment required at least 3 tools, including one "prediction" tool with derived logic. I implemented:

1. get_current_weather - raw current conditions

2. get_forecast - raw forecast data

3. predict_umbrella_needed - prediction (applies threshold logic to precip data)

4. get_travel_recommendation - extended prediction (multi-factor scoring: temp, precip, wind)

Both #3 and #4 demonstrate "derived judgment" rather than passthrough, but #3 is simpler and directly satisfies the assignment requirement.

Tool function quality

• Docstrings: All tools have detailed Args/Returns docstrings matching the style in alpaca_mcp_server.py

• Error handling: Bad locations, invalid dates, and API failures return clean error dicts (no stack traces)

• Thin tool functions: All business logic is in weather_broker.py; MCP tool functions are 2-5 lines (just call broker + log)

Secrets management

Open-Meteo requires no API key, so no secrets setup is needed. If you switch to WeatherAPI.com or another service:

1. Create a Databricks secret scope: databricks secrets create-scope weather

2. Store your API key: databricks secrets put-secret weather api-key

3. Uncomment the env: section in app.yaml

4. Update weather_broker.py to fetch the key via WorkspaceClient().secrets.get_secret()

Extending this project (stretch ideas not implemented)

• Severe weather alerts - Add a tool that calls the National Weather Service API for US locations

• Historical weather lookup - Use Open-Meteo's historical endpoint to answer "What was the weather like in NYC last Christmas?"

• Multi-city comparison - "Which is warmer this weekend: Miami or Phoenix?" (call get_forecast for both, compare)

Author

Homework submission for Databricks Agent Bricks + MCP training
Date: 2026-08-08