Weather Prediction MCP Server

Overview

A FastMCP server exposing weather tools backed by Open-Meteo and connected to a Databricks Agent Bricks agent.

Related MCP server: weather-mcp

Architecture

User -> Agent Bricks -> Databricks App MCP Server -> weather_adapter.py -> Open-Meteo APIs

Weather API

Open-Meteo Forecast API and Geocoding API.

Authentication: none required for this non-commercial project.

Tools

get_current_weather

Returns current temperature, apparent temperature, humidity, precipitation, wind, and conditions.

get_forecast

Returns daily high/low temperature, precipitation, precipitation probability, wind, and conditions for 1 to 16 days.

predict_umbrella_needed

Applies rule-based recommendations:

• Umbrella at precipitation probability >= 40%.

• Umbrella at expected precipitation >= 1 mm.

• Umbrella for thunderstorms.

• Jacket when the high temperature is below 15 C.

• Warm layer when the low temperature is below 8 C.

Local setup

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python weather_mcp_server.py

Databricks Deployment

Deployed MCP Server App

App Name: mcp-weather-server

App URL: https://mcp-weather-server-7474660615522196.aws.databricksapps.com

MCP Endpoint: https://mcp-weather-server-7474660615522196.aws.databricksapps.com/mcp

Status: ✅ Running

Source Code Path: /Workspace/Users/bchandra.ry@gmail.com/weather-mcp

Note: The app name MUST start with "mcp-" to be visible in Agent Bricks Custom MCP Server dropdown.

Agent Bricks Configuration

Agent URL: https://dbc-81d3c679-2bf9.cloud.databricks.com/ml/bricks/sa/build/786f6cdf-f00b-443b-a0f9-180fe55ac3cc?o=7474660615522196

System Prompt: See agent_system_prompt.txt

Tools Registered:

• get_current_weather

• get_forecast

• predict_umbrella_needed

The agent is configured in AI Playground under Tools > MCP Servers > Custom MCP Server.

Agent Demonstration

The following examples demonstrate the WeatherGuide agent successfully calling the MCP server tools:

Example 1: Current Weather Query

User Question: "What's the weather like in Chicago right now?"

Tools Called:

• get_current_weather(location="Chicago")

Agent Response: The agent retrieved current conditions from Open-Meteo showing:

• Temperature: 24.2°C (feels like 26.3°C)

• Conditions: Clear sky

• Humidity: 75%

• Wind: 9.9 km/h

Example 2: Multi-Day Forecast

User Question: "Will it rain in Austin this weekend?"

Tools Called:

• get_forecast(location="Austin", days=5)

Agent Response: The agent analyzed the 5-day forecast and reported:

• Weekend forecast shows overcast and partly cloudy conditions

• Precipitation probability: 1-3% (very low)

• Temperatures: Highs of 36-40°C, lows of 24-26°C

• Conclusion: No rain expected in Austin this weekend

Example 3: Recommendation/Prediction

User Question: "Should I bring a jacket to San Francisco tomorrow?"

Tools Called:

• predict_umbrella_needed(location="San Francisco", forecast_date="2026-08-09")

Agent Response: The agent applied the rule-based decision logic:

• Date: August 9, 2026

• Temperature: 26.3°C / 13.7°C

• Conditions: Fog

• Precipitation: 0.0mm (2% chance)

• Umbrella needed: No

• Jacket recommendation: Not needed (high temp above 15°C threshold)

All three examples show the agent:

1. ✅ Correctly calling the appropriate MCP tool

2. ✅ Parsing the tool response

3. ✅ Providing natural language answers based on real weather data

4. ✅ Following the system prompt rules (no guessing, always using tools)

Error handling

Invalid locations, invalid dates, API errors, and unsupported forecast ranges return structured errors. The agent is instructed not to guess when a tool fails.

Implementation Highlights

Code Structure

• weather_mcp_server.py: FastMCP server with 3 @mcp.tool decorated functions

• weather_adapter.py: Clean HTTP adapter handling all Open-Meteo API calls

• app.yaml: Databricks App configuration

• requirements.txt: Dependencies (fastmcp>=2.0, requests>=2.32)

• agent_system_prompt.txt: Complete agent behavior instructions

Key Features

✅ Clean separation of concerns: MCP tool functions are thin wrappers; all HTTP logic lives in the adapter

✅ Structured error handling: WeatherAPIError exceptions return clean error dicts with guidance

✅ No secrets required: Uses free Open-Meteo API (no API keys)

✅ Rule-based predictions: The predict_umbrella_needed tool applies documented thresholds:

• Umbrella if precipitation ≥ 1mm OR probability ≥ 40% OR thunderstorms

• Jacket if high temp < 15°C

• Warm layer if low temp < 8°C

✅ Location resolution: Geocoding API resolves city names to coordinates automatically

✅ Detailed docstrings: Every tool has Args/Returns documentation

GitHub Repository

Repository: https://github.com/bidhan017/weather-mcp

The app is deployed from this Git repository with automatic updates on push.

Limitations

• Forecast values can change as new model runs are published.

• Recommendations are simple threshold-based judgments, not official warnings.

• Open-Meteo attribution should be retained in the application and README.

• App name must start with "mcp-" to be discoverable in Agent Bricks Custom MCP Server list.