Weather MCP Server

The Weather MCP Server is a lightweight, protocol‑driven service that bridges AI assistants with real‑time meteorological data from the 和风天气 API. By exposing a simple, well‑defined MCP interface, it allows developers to inject up‑to‑date weather information into conversational flows without the need for custom API integrations or manual data fetching. This solves a common pain point: keeping AI assistants contextually aware of local weather conditions in a scalable, secure manner.

At its core, the server listens for requests containing a city identifier and returns a structured payload that includes temperature, humidity, wind speed, precipitation, and other granular metrics. The MCP protocol ensures that the assistant can request this data via a standardized resource call, receive a JSON response, and embed it directly into the user’s dialogue. This eliminates latency caused by separate network hops and keeps the conversation seamless.

Key capabilities include:

• Real‑time data retrieval from 和风天气, guaranteeing that assistants provide the latest forecasts and weather alerts.
• Rich, structured output with multiple climatic parameters, enabling nuanced responses such as “It’s 18 °C with a 70% chance of rain in Shanghai.”
• SSE (Server‑Sent Events) support, allowing the server to push updates to clients when weather conditions change—useful for monitoring or alerting scenarios.
• Docker‑ready deployment, simplifying scaling and integration into existing containerised AI stacks.

Typical use cases span a wide spectrum:

• Travel assistants that suggest packing lists based on current weather.
• Smart home hubs that adjust HVAC settings in response to external temperature shifts.
• Agricultural tools that provide farmers with humidity and precipitation data for irrigation planning.
• Emergency response bots that surface severe weather warnings in real time.

Integration is straightforward for developers familiar with MCP. Once the server address and SSE mode are configured in tools like CherryStudio, an AI assistant can invoke the resource with a city name, receive structured data, and weave it into natural language responses or trigger downstream workflows. The server’s single‑image screenshot demonstrates how the assistant presents weather information directly within a conversation, highlighting its practical value.

Overall, the Weather MCP Server offers a clean, protocol‑compliant pathway to embed accurate meteorological insights into AI experiences, reducing boilerplate code and enhancing the contextual relevance of assistant interactions.