Weather MCP Server

The Weather MCP Server fills a critical gap for AI assistants that need real‑time, authoritative weather data without embedding external API calls directly in the assistant’s logic. By exposing a small set of well‑defined tools over the Model Context Protocol, it lets developers delegate weather queries to a dedicated backend that handles authentication, rate‑limiting, and data normalization for the National Weather Service (NWS) API. This abstraction keeps the assistant’s prompt logic clean while still delivering reliable, up‑to‑date forecasts and alerts.

What the Server Does

At its core, the server offers two high‑level tools:

• – Returns all current weather alerts for a specified U.S. state, identified by its two‑letter postal code (e.g., “CA” or “NY”). The tool fetches the latest alert feed from NWS, parses it into a structured JSON format, and forwards it to the assistant for contextual use.

• – Provides a detailed forecast for any geographic coordinate pair. By passing latitude and longitude values, the tool queries NWS’s point‑based forecast endpoint, transforms the response into a GeoJSON‑compatible structure, and makes it available to the assistant.

Both tools are implemented in TypeScript, giving developers strong typing guarantees and a maintainable codebase. The server handles network errors gracefully, implements exponential back‑off for transient failures, and caches responses to respect NWS’s rate limits.

Why It Matters

For developers building AI applications that need weather information—whether for travel planning, agriculture advice, or emergency response—the Weather MCP Server removes the friction of direct API integration. Instead of writing custom parsers or managing API keys, developers can simply call the and tools through MCP. This leads to:

• Consistency – All weather data is sourced from a single, authoritative provider (NWS), ensuring uniformity across applications.
• Scalability – The server can be deployed behind a CDN or in a cloud function, automatically scaling with request volume while respecting NWS’s usage policies.
• Security – Sensitive API credentials remain on the server side, never exposed to client code or the assistant’s prompt logic.

Key Features & Capabilities

• GeoJSON Support – Forecast data is returned in GeoJSON, enabling easy integration with mapping libraries or GIS tools.
• TypeScript Interface – A clear set of type definitions () makes it straightforward to extend or modify the data contracts.
• Zero‑Configuration – The server comes pre‑configured to connect to NWS; no additional setup is required beyond installing dependencies.
• Extensible Toolset – While the current release includes two tools, the architecture allows for quick addition of new weather endpoints (e.g., radar imagery or historical data).

Real‑World Use Cases

1. Travel Assistants – A chatbot can ask a user for their destination and then automatically pull the latest forecast and any active alerts to recommend packing lists or safe travel times.
2. Agricultural Advisory Systems – Farmers can query the server for upcoming frost warnings or precipitation forecasts, allowing them to schedule irrigation or protect sensitive crops.
3. Disaster Response Platforms – First‑responders can quickly obtain all active alerts for a region, helping to coordinate evacuation routes or resource deployment.
4. Smart Home Automation – Devices can adjust heating, cooling, or window settings based on real‑time forecast data fetched via MCP.

Integration with AI Workflows

Developers embed the Weather MCP Server into their AI pipelines by listing its tools in the assistant’s configuration. The assistant can then invoke or as part of a larger reasoning chain, receiving structured JSON that can be directly incorporated into responses or further processed by downstream services. Because the server adheres to MCP standards, it works seamlessly with any assistant framework that supports the protocol, from Claude to OpenAI’s own tool integration layer.

Unique Advantages

• Single Point of Truth: All weather data is funneled through NWS, eliminating discrepancies that arise from using multiple providers.
• Developer‑Friendly: The TypeScript codebase, clear interfaces, and minimal configuration make onboarding fast for teams already familiar with Node.js.
• Protocol‑Ready: By leveraging MCP, the server can be swapped out or upgraded without changing the assistant’s core logic—future proofing against API changes.

In summary, the Weather MCP Server empowers AI developers to deliver accurate, timely weather insights with minimal overhead, enabling richer user experiences across a wide range of domains.