Weather MCP Agent

This project builds a natural-language weather assistant with Databricks Agent Bricks and an external Model Context Protocol (MCP) server.

What we are building

1. Weather MCP server

Expose weather tools from a FastMCP server, following the same pattern as mcp_server/alpaca_mcp_server.py. The tools will be backed by a free weather API such as Open-Meteo, which does not require a paid tier, API key, or credit card to get started.

2. Weather broker/adapter

Add a broker module with the same responsibility as alpaca_broker.py. It will:

• Call the external weather API.

• Handle HTTP requests and response parsing.

• Return clean, predictable dictionaries to the MCP server.

MCP tool functions should remain thin and delegate API and parsing logic to this adapter.

3. Databricks Agent Bricks agent

Create a Databricks Agent Bricks agent that uses the MCP server as an external tool. The agent should answer natural-language questions such as:

• “Will it rain in Chicago tomorrow?”

• “Should I bring a jacket to Austin this weekend?”

The agent will translate the user’s question into the appropriate weather tool call and present the result in a useful, conversational response.

4. Dashboard

Provide a small application similar to the reference dashboard/ that calls the weather MCP server and shows current conditions, a multi-day forecast, and the explainable umbrella recommendation in one view.

Related MCP server: weather-prediction-mcp

Intended architecture

User question
     |
     v
Databricks Agent Bricks agent
     |
     v
FastMCP weather server
     |
     v
Weather broker/adapter
     |
     v
Free weather API (Open-Meteo)

Weather MCP server
     ^
     |
Weather dashboard (FastMCP client)

The separation between the MCP server and the broker keeps tool definitions simple, makes the HTTP integration easier to test, and leaves room to change the weather provider later.

MCP server implementation

The server is in mcp/ and uses FastAPI as the ASGI host for a streamable-HTTP FastMCP application. The MCP endpoint is /mcp/; /healthz is a lightweight liveness endpoint for Databricks Apps.

Available tools

Locations can be city names, postal codes, or coordinates in "latitude,longitude" form. The adapter resolves locations and performs all HTTP calls; MCP functions only delegate to it. Open-Meteo is used without an API key, paid tier, or credit card, and supports roughly 10,000 calls per day for non-commercial use.

Run locally

uv sync
cd mcp
uv run python weather_mcp_server.py

Then check http://localhost:8000/healthz. An MCP client should register http://localhost:8000/mcp/ as the external MCP URL.

Deploy and register with Agent Bricks

Deploy mcp/app.yaml as its own Databricks App. Register the deployed app's /mcp/ URL as an external MCP server in Agent Bricks, then use the system prompt in mcp/AGENT_SYSTEM_PROMPT.md. The prompt requires tool-backed answers, asks for location clarification when needed, and prevents the agent from guessing during API failures.

Suggested validation questions after registration:

1. “What is the weather in Chicago right now?”

2. “Will it rain in Chicago tomorrow?”

3. “Should I bring an umbrella to Austin this weekend?”

Dashboard implementation

The dashboard in dashboard/ is a separate FastAPI Databricks App. It calls the deployed MCP endpoint with FastMCP's client, so its current conditions, forecast, and recommendation panels use the same tools as Agent Bricks and do not duplicate Open-Meteo parsing.

Run both apps locally:

# Terminal 1
PORT=8000 uv run python mcp/weather_mcp_server.py

# Terminal 2
WEATHER_MCP_URL=http://127.0.0.1:8000/mcp/ PORT=8001 \
  uv run python dashboard/app.py

Open http://localhost:8001. For Databricks deployment, deploy dashboard/app.yaml as a separate App and set WEATHER_MCP_URL to the deployed MCP App's /mcp/ URL.