"How to create Markdown files locally" matching MCP servers:

A minimal MCP server using FastMCP to demonstrate tool creation, including a get_weather example.

An advanced Model Context Protocol server that integrates real-time weather data, local file system navigation, and geographic information with AI-powered analysis tools. It enables users to perform complex data evaluations and manage files while accessing live climatic and demographic data through compatible MCP clients.

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.

An MCP server for US electricity demand and weather data that provides tools, resources, and prompts to query hourly demand, daily demand, peak demand, and demand vs temperature across regions, all running locally with a bundled SQLite database.

Enables AI assistants to look up live US river stage, streamflow and water temperature from USGS gauges, along with NWS flood stages, current flood category and crest forecasts, plus daily history and nearby-gauge searches by place or coordinates. Needs no API key and runs hosted or locally.

Solar vitamin D: whether the sun where you are can make vitamin D right now, how many minutes your Fitzpatrick skin type needs for a target dose, which months of the year synthesis is possible at your latitude, and how much a sun session produced. Computed from solar geometry, a clear-sky UV model with ozone and altitude, and live Open-Meteo UV data.

Provides six general-purpose utilities (geocoding, reverse geocoding, weather, web search, URL-to-Markdown, secure random) as paid MCP tools, with each call costing $0.01 USDC on Base mainnet.

MCP server for worldwide weather data, using high-resolution GeoSphere Austria data for the Alpine region and Open-Meteo elsewhere. Provides current conditions, hourly forecasts, and daily outlooks with compact emoji-markdown output.

Archives Garmin Connect health data locally (steps, HR, sleep, body battery) as JSON, no cloud, and also pulls context data (weather, pollen, air quality). Built-in MCP server (with SQLite cache) exposes the archive for querying via LLM tool calls.

Model Context Protocol server: plug AEMET into Claude Desktop, Cursor, Windsurf or any MCP client. Runs locally over stdio.

Tracks how long you've been indoors, nags you to touch grass, detects frustration, and recommends nearby parks with live weather.

Enables AI assistants to check live air quality in Poland, including the nearest official GIOŚ monitoring stations with the air quality index and hourly PM2.5, PM10, NO2, O3 and SO2 readings, plus smog warnings for the voivodeship and a 3-day Copernicus CAMS forecast of particulate matter and pollen. Can be used via a public no-key endpoint or run locally.

Enables riders to ask an assistant for wind-sport forecasts at a named spot, combining AROME, ICON, ECMWF, GFS, UKMO and other models into one hourly table with consensus, spread, gust diagnosis, and onshore/offshore safety relative to the beach orientation. It also finds spots, lists which models apply locally, and returns rideable daytime windows within a given knot range.

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.

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.

Enables AI agents to fetch real-time weather data for any location using the OpenWeatherMap API. Demonstrates how to build a simple MCP server that exposes weather information as a tool for LLMs.

An MCP server that provides web search capabilities through the Tavily API and weather data retrieval tools. It demonstrates how to integrate external APIs into AI applications and build agentic workflows with LangGraph.

A Python-based MCP server that predicts tomorrow's weather. It can be used remotely via uvx or locally with uv.

A TypeScript-based MCP server that facilitates note management and weather queries. It enables users to create, access, and summarize notes while providing integration for services like flomo.

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.

Demonstrates how to integrate Model Context Protocol with Server-Sent Events (SSE) in a FastAPI web application, including a weather service example with tools for getting forecasts and alerts.

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.

A TypeScript-based Model Context Protocol server that enables Claude Desktop to fetch real-time weather data via external weather APIs. It provides a simple integration for users to query current weather information through natural language interactions.

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 LLM clients to fetch weather and create, list, get, and delete notes through zod-validated tools over stdio or Streamable HTTP from one codebase, with production-grade error handling and hermetic tests.

Enables users to access five astrology tools via JSON-RPC, including natal chart, sun sign, market pulse, weather, and combined celestial conditions, integrating Free Astrology API, CoinGecko, and Open-Meteo.

Provides access to Visual Crossing Weather Timeline API for weather data retrieval via natural language queries.

Enables natural language access to ERA5 climate data from the Copernicus Climate Data Store. Supports downloading monthly means and inspecting NetCDF files.

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.