Openmeteo Weather MCP

OpenMeteo Weather MCP Server

The OpenMeteo Weather MCP server bridges the gap between AI assistants and real‑time weather data by exposing hourly forecasts through a lightweight, protocol‑compliant interface. It pulls information from the public Open‑Meteo API—an open, no‑API‑key service that delivers high‑resolution meteorological data—and presents it as a set of MCP resources. Developers can now query current conditions, precipitation probabilities, wind speeds, and temperature trends directly from their AI workflows without handling API keys or parsing raw JSON responses.

Why This Server Matters

Weather data is a common requirement for conversational agents, planning assistants, and IoT applications. Existing integrations often involve complex authentication or costly paid APIs. This MCP server solves those pain points by providing a free, open‑source bridge that requires no credentials. It also abstracts away the intricacies of coordinate handling, unit conversions, and data caching, allowing developers to focus on higher‑level logic rather than low‑level API quirks.

Core Capabilities

• Hourly Forecast Retrieval: Clients can request a full 48‑hour forecast for any latitude/longitude pair, receiving structured data on temperature, precipitation, wind, and more.
• Granular Time Intervals: The server supports sub‑hourly resolution where available, enabling fine‑grained event planning.
• Unit Flexibility: All measurements are returned in SI units by default, but the MCP interface can expose alternate units through simple parameter overrides.
• Caching and Rate‑Limiting: To respect Open‑Meteo’s usage policies, the server implements a short‑term cache that reduces redundant external calls while keeping data fresh.
• Extensible Resource Model: Additional weather metrics (e.g., humidity, UV index) can be added to the resource schema without changing client code.

Real‑World Use Cases

• Travel Planning: An AI travel assistant can ask the server for weather along a route, then suggest packing lists or activity adjustments.
• Agricultural Advisory: Farmers’ assistants can retrieve hourly precipitation forecasts to schedule irrigation or protect crops.
• Event Management: Outdoor event planners can monitor wind and temperature trends to make safety decisions.
• Smart Home Automation: Home assistants can trigger HVAC or window controls based on forecasted temperature swings.

Integration with AI Workflows

The MCP server fits seamlessly into existing AI pipelines. A client such as Claude can issue a resource request to the endpoint, receive structured weather data, and immediately incorporate it into natural language responses or downstream decision‑making modules. Because the server follows MCP conventions, developers can compose multi‑step chains—e.g., “fetch weather, compute risk score, generate recommendation”—without leaving the MCP ecosystem.

Distinctive Advantages

• Zero‑Cost, No‑Auth: Eliminates the need for API keys or paid subscriptions.
• Protocol‑First Design: Adheres strictly to MCP standards, ensuring compatibility with any compliant AI client.
• Open‑Source Transparency: The entire codebase is publicly available, allowing developers to audit, extend, or host the server on their own infrastructure.
• Ease of Deployment: A single command launches the server, making it accessible even in constrained environments.

In summary, the OpenMeteo Weather MCP server empowers AI assistants to deliver timely, accurate weather insights with minimal overhead. Its lightweight design, robust feature set, and open‑source nature make it an ideal choice for developers seeking reliable meteorological data within a unified MCP framework.