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Weather-Prediction MCP Server

by gabrieljba
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Python
Remote

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)

Related MCP server: Weather MCP Server

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

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

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

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