"Weather Inquiry" matching MCP servers:

A simple MCP server demonstrating tools, resources, and prompts for weather data, built during an Agentic AI Internship.

Automates trade surveillance support workflows by parsing inquiry emails, searching SQL configs and Java code using keyword-based metadata annotations, executing reports, and generating comprehensive responses.

A step-by-step workshop for building an MCP server that provides weather tools to AI agents, enabling secure API key usage.

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.

A basic MCP server adapted from the official quickstart guide that provides weather data functionality and works with OpenAI chat completions API. Demonstrates MCP server setup with configuration examples for Claude Desktop and development tools.

A learning project demonstrating how to build MCP servers with Python, featuring weather query tools that showcase custom tool creation, configuration, and integration with AI assistants.

A simple service using Model Context Protocol (MCP) to provide weather information for multiple cities including Fukuoka, Tokyo, Osaka, Moscow, and New York.

A simple educational MCP server implemented in Node.js that allows users to fetch weather information for cities using the Open-Meteo API.

A tutorial-based MCP server that fetches real-time weather data from the OpenWeatherMap API for specified locations. It serves as a practical example for implementing the Model Context Protocol using Python and the FastMCP library.

Serves Open-Meteo weather forecasts from a standalone stdio MCP process, letting any MCP client fetch forecast data without importing the harness into the plugin process. It pairs this with a comparison suite that measures in-process plugin behavior against the signed MCP bridge, documenting isolation and trust-ladder evidence.

Enables deploying a stateless remote MCP server on Cloudflare Workers without authentication, with tools that can be connected to Cloudflare AI Playground or local clients via mcp-remote.

A template MCP server that provides weather forecasting functionality by fetching current and hourly temperature data from the Open Meteo API for any given latitude and longitude coordinates.

A simple MCP server that provides weather tools, deployable with Docker, and designed to be used with Claude Desktop via a WSL bridge.

Provides weather information through MCP tools integrated with a FastAPI backend, enabling users to query current weather for single or multiple cities and check API health status.

A Model Context Protocol (MCP) server built with the mcp-framework to provide weather-related tools and data to AI clients. It enables integration of weather capabilities and custom tools into the MCP ecosystem for use with platforms like Claude Desktop.

A local, no-auth MCP server that exposes mocked weather forecast and supported cities tools over Streamable HTTP, enabling developers to test MCP integration with hosts like ChatGPT, Claude, and MCP Inspector.

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.

Claude Code plugin and MCP server that nudges you to take outdoor breaks based on local weather, sunset timing, and session streaks. Exposes check_grass_conditions, suggest_activity, log_touch_grass, and get_stats tools — fully local, no API keys, no cloud storage.

A learning-focused MCP server that provides tools for current time, arithmetic, random quotes, mock weather, and UUID generation, demonstrating the Model Context Protocol end-to-end.

A comprehensive MCP server and client implementation in Python exposing tools (calculator, string utilities, simulated weather), resources (file reader, system info), and prompts (code review, summarization) with security hardening and full configuration management.