Currency Weather News MCP Server

Overview

The Test MCP Server is a lightweight Python-based MCP (Model Context Protocol) implementation that exposes three real‑time data services to AI assistants: the current U.S. dollar exchange rate, a short‑term weather forecast, and news headlines from the past week. By packaging these services behind a single MCP endpoint, developers can enrich their AI workflows with up‑to‑date financial, environmental, and informational context without the need for custom API integrations.

This server solves a common pain point in AI‑powered applications: the friction of aggregating data from disparate third‑party APIs. Each resource—currency rates, weather, and news—is retrieved through well‑known public services (exchangerate-api.com, OpenWeatherMap, and NewsAPI.org). The MCP server normalizes the responses into a consistent schema, allowing an assistant to request any of these resources via simple prompts or tool calls. The result is a seamless, unified interface that reduces the overhead of authentication, rate‑limit handling, and data transformation.

Key capabilities include:

• Resource discovery: The server advertises its three resources (, , ) through the MCP endpoint, enabling clients to programmatically discover what data is available.
• Prompt templates: Pre‑defined prompts guide assistants on how to phrase requests, ensuring that the underlying API calls are constructed correctly.
• Sampling and caching: The server can cache recent responses to reduce external API traffic, and offers configurable sampling parameters for statistical queries (e.g., average temperature over the week).
• Docker support: Ready‑to‑run Docker images simplify deployment in containerized environments, making the server production‑ready without manual setup.

Real‑world use cases abound. A financial chatbot can instantly provide currency conversion rates alongside weather alerts that might affect travel plans, or a news aggregator can surface headlines relevant to a user’s current location and economic context. Developers building travel assistants, investment advisors, or smart home hubs can embed this MCP server to supply contextual data without writing bespoke connectors.

Integrating the Test MCP Server into an AI workflow is straightforward: an assistant sends a tool call to one of the exposed resources, receives a structured JSON payload, and incorporates it into its response generation. Because the server follows MCP conventions, any compliant client—whether a custom script or a commercial platform—can interact with it out of the box. The result is richer, more informed AI interactions that leverage live external data with minimal effort.