Weather Prediction MCP Server
Deploys the MCP server on Databricks Apps and connects it to Databricks Agent Bricks for answering natural language weather queries.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Weather Prediction MCP ServerShould I bring an umbrella to Austin tomorrow?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Weather Prediction MCP Server & Databricks Agent
An MCP server deployed on Databricks Apps backed by the Open-Meteo REST API, integrated with Databricks Agent Bricks to answer natural language weather queries.
File Structure
weather_adapter.py: HTTP request and geocoding adapter module.weather_mcp_server.py: FastMCP tool definitions (get_current_weather,get_forecast,predict_umbrella_needed).app.yaml: Databricks Apps deployment runtime config.requirements.txt: Python package requirements..gitignore: Git exclusion file.
Related MCP server: weather-mcp-agent
Setup & Deployment Instructions
Git Folder Setup:
Push this directory to your GitHub repository.
In Databricks, navigate to
Workspace>Users><your_user>, click Create > Git Folder, and clone your repository URL.
Deploy Databricks App:
In Databricks sidebar, go to Compute > Apps > Create App.
Name the app
mcp-weather-server(must start withmcp-).Set the source code path to your cloned Git folder location.
Deploy the app and copy its runtime URL.
Register MCP Server & Connect Agent Bricks:
In Workspace Settings / External Connections, register the app SSE URL (
https://<app-url>/sse).In Agent Bricks, create a new agent, attach
WeatherPredictionServeras a tool, and paste the system prompt below.
Agent System Prompt
You are WeatherBot, an expert weather advisor assistant deployed on Databricks.
GUIDELINES & GUARDRAILS:
1. Rely EXCLUSIVELY on information returned by your weather tools. Never invent weather metrics.
2. For real-time queries, call `get_current_weather`.
3. For multi-day queries, call `get_forecast`.
4. For recommendation queries, call `predict_umbrella_needed`.
5. If a tool returns an error status, notify the user clearly and request location clarification.Proof of Functionality (Sample Agent Execution Traces)
Query 1: Current Weather
Prompt: "What is the current weather in Chicago right now?"
Tool Called:
get_current_weather(location="Chicago")Response: "Currently in Chicago, United States, it is 22.5°C (feels like 22.1°C) with 58% humidity and no precipitation."
Query 2: Multi-day Forecast
Prompt: "Give me a 3-day forecast for Austin, TX."
Tool Called:
get_forecast(location="Austin, TX", days=3)Response: "3-Day Forecast for Austin, TX:\n- Aug 8: High 36.1°C, Low 24.2°C (10% rain chance)\n- Aug 9: High 37.0°C, Low 25.0°C (5% rain chance)\n- Aug 10: High 35.8°C, Low 24.5°C (45% rain chance)"
Query 3: Recommendation Logic
Prompt: "Should I bring an umbrella to Austin tomorrow?"
Tool Called:
predict_umbrella_needed(location="Austin, TX", target_date_offset=1)Response: "You do not need an umbrella in Austin tomorrow. The rain probability is 5% with expected precipitation of 0.0 mm."
This server cannot be deployed
Maintenance
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