MCP Weather Server

Weather MCP Server Overview

The Weather MCP server fills a common gap for AI‑assisted development: the need to access up‑to‑date, location‑specific meteorological data without leaving the conversational or programmatic flow. By exposing a lightweight API that returns current conditions, forecasts, and historical weather metrics, the server lets Claude or other MCP‑compatible assistants answer climate‑related questions in real time and embed that data directly into applications or reports.

For developers, this means a single, well‑defined resource that can be queried from any client with minimal boilerplate. The server accepts simple JSON payloads specifying latitude, longitude, and optional time ranges, then translates those into a structured response that includes temperature, humidity, wind speed, precipitation probability, and a short textual summary. Because the data is delivered in a consistent format, it can be immediately fed into downstream ML models, dashboards, or natural‑language generation pipelines without additional parsing logic.

Key capabilities of the Weather MCP server include:

• Real‑time retrieval of current conditions and short‑term forecasts (up to 48 hours).
• Historical data lookup for trend analysis or model training.
• Unit flexibility, allowing temperatures in Celsius or Fahrenheit and wind speeds in km/h or mph.
• A fallback mechanism that returns cached values if the external provider is temporarily unavailable, ensuring robustness in production workflows.

Typical use cases span from conversational agents that answer “What’s the weather like in Paris tomorrow?” to embedded systems that trigger irrigation schedules when humidity drops below a threshold. In analytics platforms, the server can supply weather covariates for predictive models of sales or energy consumption. Because the MCP interface abstracts away authentication and rate‑limiting concerns, developers can focus on business logic rather than API integration details.

What sets this server apart is its declarative resource definition: the MCP specification allows a single line in the schema to expose all of the above functionality, and the assistant can then invoke it with a natural‑language prompt such as “Get me the weather for New York next week.” This tight coupling between AI intent and external data stream simplifies end‑to‑end development, reduces latency, and improves the reliability of weather‑dependent features in AI‑powered applications.