Weather Server

Weather Server MCP

The Weather Server is a lightweight, TypeScript‑based MCP server that turns real‑time meteorological data into an AI‑friendly resource. It bridges the gap between external weather APIs and AI assistants by exposing structured weather information as resources, offering actionable tools to fetch or update that data, and providing a prompt for concise weather summaries. Developers can plug this server into Claude or any MCP‑compatible assistant to give the model instant, accurate weather context without manual data curation.

Solving a common AI integration problem

When an assistant needs up‑to‑date weather information, the typical workflow involves calling a third‑party API directly from application code or embedding static data into prompts. Both approaches are brittle: direct calls tie the assistant to network reliability, while static data quickly becomes stale. The Weather Server abstracts these concerns by caching weather state internally and exposing it through a stable URI scheme. This means the assistant can request weather data as if it were a local file, while the server transparently handles external API lookups and updates.

Core value for AI developers

By turning weather data into a first‑class resource, the server lets LLMs query and manipulate environmental information using familiar MCP verbs (e.g., , , ). Developers can therefore write prompts that reference a location () and let the model decide whether to fetch fresh data or rely on cached values. The server’s tools (, ) give the model controlled, parameterized access to external APIs, reducing boilerplate code and enabling more natural conversational interactions.

Key features explained

• Resource namespace – All weather entries live under the scheme, each with a clear URI, location metadata, temperature readings, and JSON payloads for structured queries.
• Fetching tool – takes a location string and retrieves the latest data from an external provider, automatically populating the server’s state.
• Updating tool – lets an assistant push new weather data back into the server, useful for testing or simulating forecast changes.
• Summarization prompt – aggregates all current entries and produces a concise, LLM‑ready prompt that can be fed into the assistant for natural language summaries or trend analysis.

Real‑world use cases

• Travel assistants – Provide users with current weather at destination cities or along travel routes, updating dynamically as the trip progresses.
• Agricultural bots – Pull local temperature and humidity data to advise farmers on irrigation or pest control decisions.
• Event planners – Generate weather forecasts for scheduled outdoor events, allowing the assistant to suggest contingency plans.
• Smart home integration – Feed weather conditions into home automation scripts, enabling temperature‑based HVAC adjustments.

Seamless workflow integration

The server is designed to be a drop‑in MCP component. Once registered in the assistant’s configuration, developers can reference weather resources directly in prompts or invoke tools with simple parameter lists. The MCP Inspector utility simplifies debugging, allowing developers to inspect resource listings, tool invocations, and prompt outputs in real time. Because the server communicates over stdio, it can be run as a standalone process or embedded within larger AI orchestration pipelines.

Standout advantages

• Unified URI scheme – Treats weather data like any other file or resource, simplifying prompt design.
• Extensible toolset – Developers can add custom tools (e.g., ) without altering the core server logic.
• Decoupled API key management – External API credentials are stored in a file, keeping secrets out of prompts and code.
• Built‑in summarization – The prompt streamlines the transition from raw data to natural language, reducing LLM workload.

In summary, the Weather Server MCP provides a robust, developer‑friendly bridge between real‑time meteorological services and AI assistants. Its resource‑centric architecture, coupled with expressive tools and summarization prompts, empowers developers to build contextually aware applications that can fetch, update, and interpret weather data with minimal friction.