Workflows MCP Server

The Workflows MCP Server is a lightweight, Model Context Protocol (MCP) service that turns raw weather data into conversational insights. By exposing a single tool and an accompanying prompt template, it bridges the gap between static APIs (like OpenWeatherMap) and natural‑language AI assistants. Developers can plug this server into any agent or LLM workflow, enabling the assistant to fetch precise meteorological information and then translate it into friendly prose without leaving the MCP ecosystem.

At its core, the server follows a simple yet powerful architecture. When an agent invokes with a city name, the server queries OpenWeatherMap for current conditions. The raw JSON payload is then forwarded to an LLM, which interprets the data and returns a human‑readable description such as “It’s 72°F with scattered clouds in New York.” This two‑step pipeline—data retrieval followed by LLM interpretation—ensures that users receive both accuracy and readability, turning numbers into contextually rich statements.

Key capabilities include:

• Tool exposure: The tool is registered with the MCP runtime, making it discoverable by any compliant client.
• Prompt templating: The prompt template allows developers to embed the tool call directly into LLM prompts, simplifying agent design.
• Transport flexibility: The server supports HTTP and Server‑Sent Events (SSE), giving clients the choice between simple REST calls or real‑time, bidirectional streams.
• Self‑contained workflow: All logic—from API calls to LLM formatting—is encapsulated within the MCP server, reducing client-side complexity.

Real‑world use cases abound. A travel assistant can answer “What’s the weather in Tokyo tomorrow?” by calling , while a smart home system can use the friendly output to decide whether to open curtains. In customer support, chatbots can provide up‑to‑date weather forecasts without hardcoding any external logic. Because the server handles both data fetching and natural language generation, developers can focus on higher‑level conversation design rather than plumbing.

Unlike generic HTTP wrappers, this MCP server offers a standalone workflow that integrates seamlessly with any agent framework. Its unique advantage lies in the combination of a reliable data source, an LLM‑powered interpretation layer, and MCP’s standardized tool communication. This makes it a drop‑in component for developers building sophisticated, contextually aware AI assistants that need to surface real‑world information in an engaging way.