LandiWetter MCP Server

The LandiWetter MCP Server fills a niche for developers who want instant, reliable access to Swiss weather data within AI‑driven applications. By exposing a lightweight MCP interface that queries the official LandiWetter API, the server lets assistants like Claude retrieve accurate forecasts without the need to build custom weather integrations from scratch. This is particularly valuable for applications that require location‑specific climate information—travel planners, logistics platforms, or any service that needs to adapt to local weather conditions.

At its core, the server offers two primary capabilities: searching Swiss locations and fetching detailed weather forecasts for a chosen place and date. The tool accepts a location name and returns matching Swiss locales, while the tool takes a location ID (e.g., G2661552) and an optional date, delivering hourly or daily forecast data. These tools can be invoked directly in natural language prompts, making the experience seamless for users of MCP‑compatible clients such as Claude Desktop. In addition to tools, the server defines a weather-forecast resource that can be accessed via a URI template (), enabling developers to embed weather data in structured workflows or external systems that consume resources.

The server’s design aligns closely with MCP best practices: it operates over stdio, making it straightforward to launch as a child process or via a command line. Once added in an MCP client, the server becomes instantly available as a tool or resource, allowing developers to compose complex prompts that combine weather data with other domain knowledge. For example, a travel assistant could ask for flight schedules and then automatically adjust recommendations based on the forecast for the destination city.

Key features that set LandiWetter apart include:

• Swiss‑centric data: All forecasts are sourced from the authoritative LandiWetter service, ensuring high accuracy for Switzerland.
• Dual granularity: Users can obtain either hourly or daily summaries, depending on the use case.
• Simple integration: No API keys or external configuration are required; the MCP server handles all communication internally.
• Resource URI support: Developers can reference forecasts declaratively in their applications, enabling reusable data pipelines.

Typical use cases span a broad spectrum:

• Travel and tourism apps that need to suggest activities based on weather.
• Supply chain solutions where temperature‑sensitive goods must be routed around inclement conditions.
• Smart home systems that adjust heating or outdoor lighting in response to forecasted temperatures.

By packaging these functionalities into an MCP server, LandiWetter empowers AI assistants to deliver contextually rich, location‑aware responses—turning raw weather data into actionable insights without extra engineering overhead.