How do I use Open-Meteo MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Open-Meteo MCP Server What's the current weather in Tokyo?" 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.

Open-Meteo MCP Server

by BilelSelmiAi2026

Overview Schema Related Servers Score Discussions

Python

Remote

Open-Meteo AI Assistant using Model Context Protocol (MCP)

Overview

The Open-Meteo AI Assistant is a production-style Python project demonstrating how to build an AI application using the Model Context Protocol (MCP).

Unlike a traditional CRUD application, this project exposes weather capabilities through an MCP Server while an AI-powered MCP Client discovers available tools, lets an LLM decide which tool to invoke, executes the selected tool, and transforms structured responses into natural language.

The project also demonstrates Server-Initiated LLM Sampling, where the MCP server requests the connected AI client to generate content while executing a tool.

Architecture

                              User
                                │
                                ▼
                      OpenAI Responses API
                                │
                                ▼
                  assistant/ai_client.py
                     (AI Assistant / MCP Client)
                                │
                                ▼
                     MCP Protocol (stdio)
                                │
                                ▼
                  Open-Meteo MCP Server
                          (main.py)
                                │
            ┌───────────────────┼────────────────────┐
            │                   │                    │
            ▼                   ▼                    ▼
      get_weather       compare_weather     generate_packing_advice
                                                    │
                                                    ▼
                                          weather_client.py
                                                    │
                                                    ▼
                                            Open-Meteo REST API
                                                    │
                                                    ▼
                                      Live weather / forecast data
                                                    │
                                                    ▼
                                   Server-Initiated LLM Sampling
                                                    │
                                                    ▼
                                     assistant/ai_client.py
                                      (sampling callback)
                                                    │
                                                    ▼
                                               OpenAI Model

Project Structure

open-meteo-mcp/
│
├── assistant/
│   ├── client.py
│   ├── ai_client.py
│   └── test_llm.py
│
├── tests/
│
├── main.py
├── weather_client.py
├── weather_models.py
├── errors.py
│
├── .env.example
├── .gitignore
├── pyproject.toml
├── uv.lock
└── README.md

Features

The MCP server currently exposes the following tools:

Tool	Description
`search_city`	Search for a city
`get_weather`	Retrieve current weather
`get_weather_forecast`	Retrieve multi-day weather forecast
`compare_weather`	Compare weather between two cities
`find_warmest_day`	Find the warmest forecast day
`get_packing_context`	Return forecast data for packing recommendations
`generate_packing_advice`	Demonstrates Server-Initiated LLM Sampling

Technologies

Python 3.14
Model Context Protocol (MCP)
OpenAI Responses API
Open-Meteo REST API
httpx
Pydantic
pytest
uv

Installation

Clone the repository:

git clone <repository-url>
cd open-meteo-mcp

Install dependencies:

uv sync

Activate the virtual environment:

source .venv/bin/activate

Create a .env file:

OPENAI_API_KEY=your-api-key
OPENAI_MODEL=gpt-4.1-mini

Running the Project

Related MCP server: Weather MCP Server

Start the MCP Inspector

uv run mcp dev main.py

The Inspector allows you to:

Discover available MCP tools
Execute tools manually
Inspect schemas
Debug tool responses

Run the AI Assistant

uv run python assistant/ai_client.py

Example:

You:
Compare the weather in Tunis and Munich

Assistant:
Tunis is currently warmer than Munich by 7.5°C.

Example:

You:
What should I pack for three days in Rome?

Assistant:
For your three-day trip to Rome...

Application Workflow

Standard MCP Tool Calling

User
    │
    ▼
OpenAI selects an MCP tool
    │
    ▼
MCP Client
    │
    ▼
MCP Server
    │
    ▼
Open-Meteo API
    │
    ▼
Structured response
    │
    ▼
OpenAI generates the final answer

Server-Initiated LLM Sampling

This project includes an educational implementation of Server-Initiated LLM Sampling.

Instead of the AI client generating all responses itself, the MCP server can request the connected client to invoke an LLM while executing a tool.

Workflow:

User
    │
    ▼
OpenAI selects generate_packing_advice
    │
    ▼
MCP Client
    │
    ▼
MCP Server
    │
    ▼
Open-Meteo API
    │
    ▼
Forecast retrieved
    │
    ▼
ctx.session.create_message(...)
    │
    ▼
Sampling Callback
    │
    ▼
OpenAI generates packing advice
    │
    ▼
Result returned to the MCP Server
    │
    ▼
Final response returned to the user

Note: Server-Initiated LLM Sampling is deprecated in the MCP 2026-07-28 specification. It is included here for educational purposes to demonstrate advanced MCP capabilities.

Example Queries

What's the weather in Paris?

Compare the weather between Tunis and Munich.

Find the warmest day in Rome this week.

What should I pack for five days in Rome?

Running Tests

Run the complete test suite:

uv run pytest -v

Compile all Python files:

uv run python -m py_compile \
main.py \
weather_client.py \
weather_models.py \
errors.py \
assistant/client.py \
assistant/ai_client.py

Learning Outcomes

This project demonstrates:

Building an MCP Server
Building an MCP Client
MCP Tool Discovery
MCP Tool Execution
OpenAI Tool Calling
OpenAI Responses API
Server-Initiated LLM Sampling
Async Python
REST API Integration
Pydantic Validation
Error Handling
Modular Software Architecture
Separation of Concerns

Future Improvements

Possible enhancements include:

Air Quality API
Weather Alerts
Historical Weather
Docker Support
GitHub Actions CI/CD
Structured Logging
Response Caching
GitHub MCP Server
Gitea MCP Server
PostgreSQL MCP Server
Filesystem MCP Server

License

This project is intended for educational and portfolio purposes.

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

MCP Weather Server Demo
Weather Services Search
RaulCalderon
A
license
-
quality
D
maintenance
Fetches current weather information for any city using the Open-Meteo API through a simple MCP tool interface.
Last updated 2025-06-21
1,861
MIT
Weather MCP Server
Weather Services Search
akdrione
A
license
-
quality
D
maintenance
Provides weather data from OpenWeatherMap API through MCP tools and a REST API with OpenAPI support. Enables LLM agents to retrieve current weather, forecasts, and temperature ranges by city or coordinates.
Last updated 2026-04-17
32
MIT
Weather MCP Tool
Weather Services
thuong204
F
license
-
quality
D
maintenance
Provides real-time weather information for cities worldwide using the OpenWeatherMap API, accessible through natural language queries via the MCP protocol.
Last updated 2025-08-07
weather-mcp-server
Weather Services Open Data
liuweihongliuxinyu
A
license
A
quality
C
maintenance
Provides real-time weather and forecast data via MCP tools, enabling AI agents to query current weather and up to 3-day forecasts for any city.
Last updated 2026-06-23
3
MIT

View all related MCP servers

Related MCP Connectors

Openweather
OpenWeather MCP — wraps the OpenWeatherMap API (openweathermap.org)
Open Meteo
Open-Meteo MCP — weather forecast + historical reanalysis + sister APIs
Weatherapi
WeatherAPI.com MCP — wraps WeatherAPI.com (api.weatherapi.com)

View all MCP Connectors

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BilelSelmiAi2026/open-meteo-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server