Caiyun Weather MCP Server

The Caiyun Weather MCP Server turns the rich, real‑time weather data from Caiyun’s API into a set of lightweight tools that AI assistants can call directly. By exposing endpoints for current conditions, hourly and daily forecasts, and active weather alerts, it removes the need for developers to embed API calls or manage authentication within their own code. Instead, a single MCP server handles token management, request routing, and response formatting, letting the AI focus on conversation logic.

This server solves a common pain point for developers building location‑aware assistants: retrieving reliable, up‑to‑date weather information without juggling API keys or handling rate limits manually. With a single configuration line, an AI can query any of the four tools—, , , or —by passing geographic coordinates and an optional language code. The server’s compact JSON responses reduce token usage, making the data more efficient for downstream processing and reducing costs in large‑scale deployments.

Key capabilities include:

• Multi‑language support: responses can be returned in Chinese, English, or Japanese, simplifying internationalization.
• Flexible forecast horizons: hourly forecasts up to 72 hours and daily forecasts up to a week give developers granular control over the time span they need.
• Alert integration: real‑time weather warnings can be surfaced to users during a conversation, enhancing safety and relevance.
• Token‑efficient design: the server trims unnecessary fields from the API payload, conserving bandwidth and processing time.

Typical use cases span travel assistants that suggest packing lists based on upcoming weather, smart home systems that adjust HVAC settings, or emergency response bots that alert users to severe conditions. In a conversational flow, an AI might ask for the user’s location, call to provide a quick snapshot, and then offer an hourly forecast if the user plans outdoor activities.

The MCP server integrates seamlessly into existing AI workflows. Once registered, any client that speaks the Model Context Protocol can invoke these tools without additional code. Developers can chain tool calls, cache results, or combine weather data with other domain knowledge to create richer experiences. The server’s straightforward configuration and clear tool definitions make it an attractive choice for teams looking to add authoritative weather intelligence to their AI assistants with minimal overhead.