Weather Service MCP Server

The Sunwood AI Labs Weather Service MCP Server turns a lightweight note‑keeping backend into an AI‑friendly data source. By exposing notes through a custom URI scheme, the server gives Claude (or any MCP‑compatible assistant) direct access to persistent text entries that can be referenced, queried, or updated on demand. This solves the common pain point of managing stateful data in conversational agents—developers no longer need to write bespoke storage logic or maintain external databases; the MCP server handles CRUD operations, change notifications, and resource discovery automatically.

At its core, the server offers three main MCP constructs:

• Resources – Each note is a resource with a unique URI, a human‑readable name, and plain‑text content. The server’s resource schema lets clients enumerate all notes or fetch a single entry efficiently.
• Prompt – The prompt aggregates all current notes into a concise summary. An optional argument lets callers choose between a brief or detailed overview, making it easy to adapt the output to different contexts such as dashboards or quick briefs.
• Tool – The tool lets users append new notes in real time. It accepts a name and content string, updates the server state, and pushes change notifications to any listening clients. This makes the system interactive: an assistant can ask a user for new information, add it to the store, and immediately reference it in subsequent replies.

Developers benefit from the server’s tight integration with existing MCP workflows. The resources, prompts, and tools are discovered automatically by the client; no custom SDKs or API keys are required. By treating notes as first‑class resources, the server supports composable workflows—an assistant can retrieve a note, summarize it, or add new entries—all within the same conversational turn. This is particularly useful for scenarios like meeting minutes, project documentation, or weather‑related logs where the assistant needs to remember and synthesize user input over time.

Unique advantages of this MCP implementation include its simplicity—only a single resource type and two tools—making it easy to understand and extend. The custom URI scheme provides a clean, unambiguous reference model that avoids clashes with external services. Additionally, the built‑in prompt demonstrates how domain‑specific prompts can be bundled with a server, giving developers a ready‑made example of leveraging MCP’s prompt capabilities for rapid prototyping. Overall, the Sunwood AI Labs Weather Service MCP Server offers a lightweight, standards‑compliant bridge between conversational AI and persistent text data, empowering developers to build richer, contextually aware assistants without the overhead of full‑blown database integrations.