Weather-Prediction MCP Server

Weather-Prediction MCP Server

Provides real-time weather data, forecasts, alerts, and travel recommendations for US locations using the National Weather Service API, enabling agents to answer weather-related queries.

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README

Day 3 Homework: Weather-Prediction MCP Server

A FastMCP server that exposes weather-forecast tools over the Model Context Protocol, designed to be consumed by a Databricks Agent Bricks agent.

Architecture

Agent Bricks agent  --(MCP tool calls)-->  weather_mcp_server.py  --(HTTP)-->  NWS API (free, no key)
                                                    |
                                                    +--> weather_broker.py (adapter: all HTTP/parsing logic)

MCP Tools Exposed

Tool Description
get_current_weather(location) Real-time temperature, humidity, wind, conditions from nearest NWS station
get_forecast(location, days) Multi-day forecast (up to 7 days) with temp, wind, precip chance, conditions
get_weather_alerts(location) Active warnings, watches, and advisories for the location
get_travel_recommendation(location, days) Derived recommendations with threshold logic (umbrella, jacket, heat, outdoors)

Weather API

  • API: National Weather Service (NWS) — https://www.weather.gov/documentation/services-web-api
  • Auth: None required (free, public US government API)
  • Coverage: United States only
  • Geocoding: Nominatim/OpenStreetMap (free, no key) for resolving city names to coordinates

Files

File Role
weather_mcp_server.py FastMCP server with @mcp.tool decorators (thin wrappers)
weather_broker.py Adapter module — all HTTP calls, parsing, and recommendation logic
lakebase.py Lakebase connection helper (same pattern as Day 2/3, for extensibility)
app.yaml Databricks App configuration
requirements.txt Python dependencies

Secrets Required in Databricks

Secret Scope Secret Key Value Required For
database lakebase-url Base64-encoded Postgres connection URL Lakebase (optional, for future use)

Note: The NWS API requires NO API key. No weather-related secrets needed.

Deploy as Databricks App

  1. Upload this entire folder (day3-homework-mcpserver-weather/) to your Databricks workspace
  2. Create a new Databricks App pointing to this folder
  3. The app will start via python weather_mcp_server.py (as defined in app.yaml)
  4. Register the app URL as an external MCP server in Agent Bricks

Register as External MCP in Agent Bricks

  1. Go to your Databricks workspace → Agent Bricks
  2. Add a new external MCP tool connection
  3. Set the URL to your deployed app's endpoint (e.g. https://<your-app-url>/mcp)
  4. The 4 tools will be auto-discovered by the agent

Suggested Agent System Prompt

You are a weather assistant that helps users understand weather conditions
and make plans based on forecasts. You have access to real-time weather
data for US locations via the National Weather Service.

Rules:
- ALWAYS use the weather tools to get data. NEVER guess or hallucinate weather information.
- Use get_current_weather() for "what's the weather now?" questions.
- Use get_forecast() for "what will the weather be like?" questions.
- Use get_weather_alerts() when users ask about severe weather or safety.
- Use get_travel_recommendation() for planning questions ("should I bring an umbrella?", "is it a good day for hiking?").
- If a location cannot be resolved, ask the user to clarify with "City, State" format.
- Only US locations are supported. If asked about international locations, explain this limitation.
- If the API returns an error, tell the user honestly rather than making up data.
- When presenting forecasts, summarize the key points rather than dumping raw data.

Local Development

pip install -r requirements.txt
python weather_mcp_server.py
# Server starts on http://0.0.0.0:8000

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