weather-service MCP Server

The weather-service MCP server addresses a common need in AI‑assisted development: the ability to persist, retrieve, and summarize contextual information—here represented as “notes”—within an AI workflow. Instead of relying on external databases or manual data management, this server exposes a lightweight note storage system that can be accessed directly by an AI assistant through the Model Context Protocol. This integration eliminates friction when developers need to maintain a running set of observations, logs, or annotations that the assistant can reference later.

At its core, the server implements a custom URI scheme () that maps each note to a unique resource. Every note carries metadata such as a name, description, and plain‑text content, making it trivial for an AI to fetch or list notes via standard MCP resource queries. The design is intentionally minimalistic, ensuring that the server remains fast and easy to deploy while still providing a robust API for developers who need persistent state in their AI interactions.

The server’s prompt capability, , is a powerful feature that lets an assistant generate concise or detailed summaries of all stored notes. By passing an optional argument, developers can control the granularity of the output—whether a quick recap or an in‑depth analysis is required. This prompt automates what would otherwise be a manual aggregation step, enabling dynamic knowledge bases that evolve as new notes are added.

The single tool exposed, , allows the AI assistant to create new notes on demand. It accepts a name and content string, updates the server’s internal state, and broadcasts resource change notifications to any subscribed clients. This real‑time update mechanism ensures that all parties—whether developers or other assistants—see the latest information without needing to refresh manually.

In practice, this MCP server is ideal for scenarios such as:

• Project documentation: Developers can jot down quick notes during coding sessions and later ask the assistant to summarize them for a status report.
• Bug tracking: A team can log error observations, and the assistant can produce a consolidated view of all reported issues.
• Knowledge base maintenance: As new insights arise, the server stores them, and the assistant can retrieve or summarize relevant entries on demand.

By integrating seamlessly with MCP‑enabled AI assistants, the weather-service server turns ordinary text notes into actionable context. Its straightforward API, real‑time notifications, and built‑in summarization make it a valuable tool for developers who want to keep their AI workflows tightly coupled with up‑to‑date, structured information.