Weather Prediction MCP Server

Weather Prediction MCP Server

A Model Context Protocol (MCP) server that exposes weather-forecast tools, designed for consumption by a Databricks Agent Bricks agent. Deployed as a Databricks App.

Architecture

┌───────────────────────────────────────────────────────────────────┐
│  User (natural language)                                         │
│       │                                                          │
│       v                                                          │
│  Agent Bricks Agent (system_prompt.txt)                          │
│       │  MCP tool calls (streamable-HTTP)                        │
│       v                                                          │
│  ┌───────────────────────────────────────────────────────────┐   │
│  │  weather_mcp_server.py (FastMCP, Databricks App)       │   │
│  │    ├─ get_current_weather(location)                     │   │
│  │    ├─ get_forecast(location, days)                      │   │
│  │    ├─ get_travel_recommendation(location, days)         │   │
│  │    └─ compare_weather(locations, days)                  │   │
│  └───────────────────────────────────────────────────────────┘   │
│       │                                                          │
│       v                                                          │
│  ┌───────────────────────────────────────────────────────────┐   │
│  │  weather_adapter.py (HTTP layer)                        │   │
│  │    ├─ geocode(location) → lat/lon                       │   │
│  │    ├─ get_current_weather(lat, lon)                     │   │
│  │    ├─ get_forecast(lat, lon, days)                      │   │
│  │    └─ build_recommendations(forecast)                   │   │
│  └───────────────────────────────────────────────────────────┘   │
│       │                                                          │
│       v                                                          │
│  Open-Meteo API (free, no key required)                          │
│    ├─ geocoding-api.open-meteo.com/v1/search                     │
│    └─ api.open-meteo.com/v1/forecast                              │
└───────────────────────────────────────────────────────────────────┘

Weather API

Open-Meteo — chosen because:

• Zero signup, zero API keys, zero cost
• ~10,000 calls/day (non-commercial)
• Global coverage (not US-only)
• Provides geocoding, current weather, and 16-day forecasts in one API family

Tools

Recommendation Thresholds

Project Structure

WeatherMCPserver/
├── weather_adapter.py       # HTTP layer: Open-Meteo API calls + geocoding + logic
├── weather_mcp_server.py    # FastMCP server with @mcp.tool decorators
├── pyproject.toml           # UV package management
├── app.yaml                 # Databricks App deployment config
├── system_prompt.txt        # Agent Bricks system prompt
└── README.md                # This file

Setup & Deployment

Prerequisites

• Databricks workspace with Apps enabled
• UV installed (pip install uv or curl -LsSf https://astral.sh/uv/install.sh | sh)
• No API keys needed (Open-Meteo is key-free)

Local Development

# Install dependencies
uv sync

# Run the MCP server locally
uv run weather_mcp_server.py

# Server starts on http://localhost:8000
# MCP endpoint: http://localhost:8000/mcp

Deploy as Databricks App

# From the workspace, deploy the app
databricks apps create weather-mcp-server \
  --source-code-path /Workspace/Users/<your-email>/WeatherMCPserver

# Or deploy via the Apps UI:
# 1. Go to Compute > Apps > Create App
# 2. Point source to this folder
# 3. The app.yaml handles the rest

Register as External MCP Tool in Agent Bricks

1. Navigate to your Agent Bricks agent configuration
2. Add an External MCP connection:
   • URL: https://<your-app-url>/mcp
   • Transport: Streamable HTTP
3. Paste the contents of system_prompt.txt as the agent's system prompt
4. Test with: "What's the weather in Tokyo right now?"

Example Queries

Current conditions:

"What's the temperature in Berlin right now?"

Forecast:

"Will it rain in Chicago this week?"

Travel advice:

"I'm traveling to Austin, Texas for 3 days. What should I pack?"

Comparison:

"Which has better weather this weekend: Miami, LA, or Denver?"

Edge cases (handled gracefully):

"What's the weather in Xyzzyville?" → Error: location not found, suggests being more specific.

Authentication & Secrets

None required. Open-Meteo needs no API key. If you later add a keyed API (e.g. WeatherAPI.com), store the key as a Databricks secret:

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

Never hardcode keys in source files.

Lakebase Integration (Optional)

Query history can be stored in the provisioned Lakebase Postgres instance for dashboard/analytics:

Host: ep-gentle-paper-e1xaec1l.database.eastus2.azuredatabricks.net
Database: databricks_postgres
User: WeatherMCPserver

License

Internal project — Databricks learning challenge submission.