"Connecting to Gemini Flash Experimental" matching MCP servers:

A base setup for creating MCP servers that integrate with Google's Gemini CLI, including example implementations for weather data retrieval from OpenWeatherMap and basic resource/tool definitions using FastMCP.

Provides tools for managing todo lists, checking weather, and accessing user information through the Model Context Protocol. It supports local execution and remote deployment as a web-based MCP gateway integrated with Gemini.

Provides real-time weather data from Open-Meteo API, mathematical calculations, and time zone information through MCP protocol. Works with both Claude Desktop and Gemini AI CLI for natural language interactions.

Enables travel-related queries and planning including weather, train and flight information, itinerary generation, and travel knowledge retrieval via RAG, using an AI agent with MCP protocol.

MCP server connecting AI models to SRG SSR public APIs – weather, TV/radio metadata, program guide and Swiss votations/elections since 1900.

Enables retrieving Israeli weather forecasts through real browser automation and answering user questions conversationally via Gemini.

Demonstrates creating and connecting an MCP weather server using FastMCP, with support for integration with Claude Desktop, Cursor, and local LLMs to query weather alerts and information.

Enables users to query weather forecasts for Israeli cities through natural language using browser automation and Gemini.

A command-line tool that provides access to US weather data through a client-server architecture using MCP and Google's Gemini AI, allowing users to query weather alerts and forecasts using natural language.

A demonstration project for Model Context Protocol (MCP) that integrates weather services with multiple AI models (Claude, GPT, Gemini), enabling natural language queries for weather alerts and forecasts.

Provides tools for generating optimized images via Google's Gemini model and fetching weather forecasts and alerts from the National Weather Service. It enables users to create visual content and retrieve environmental data seamlessly within MCP-compatible clients.

An experimental MCP server that provides weather information tools, including temperature lookup via wttr.io, along with note-taking capabilities for learning MCP server development with GitHub Copilot.

Provides real-time weather information and multi-day forecasts using the Open-Meteo API through a robust, enterprise-grade architecture. It enables AI assistants to retrieve current conditions, 7-day forecasts, and contextual weather data for any location.

Enables users to produce explainable UK garden watering recommendations as deterministic millimetres, litres and runtime from self-reported garden details or an optional postcode, without controlling irrigation hardware.

Enables AI assistants to fetch real-time weather data, forecasts up to 10 days, and compare weather conditions between cities using WeatherAPI.com with support for both Celsius and Fahrenheit units.

Enables users to work out soil, mulch, compost, topsoil and potting mix volumes and bag counts for beds and pots, compare bagged versus bulk costs at their own prices, check what to sow each month, and see how often a town has had frost after Melbourne Cup Day. It runs as a read-only, stateless MCP server that connects directly inside Claude, ChatGPT, Cursor and VS Code.

Wraps the Tomorrow.io Weather API to provide weather data, allowing AI agents to query forecasts and historical conditions via natural language.

Enables agents to pull live overnight forecasts for grower blocks and score frost risk with an itemised, versioned engine, returning cold-window start/end hours and a run-protection, stand-by, or no-action verdict. It also lets agents list blocks and sheets, create or update idempotent decision records, and replay their SHA-384 hash chain to verify nothing was altered.

Enables AI assistants to list demo inverter sites, inspect production, pull weather context, and get deterministic panel-cleaning recommendations over MCP using synthetic solar operations data.

Enables LLMs to retrieve 24-hour weather forecasts and city information through natural language queries using city names or coordinates.