My MCP Server

Overview

My MCP Project is a lightweight, Python‑based Model Context Protocol (MCP) server that exposes a collection of common data services as AI‑ready tools. By packaging weather queries, file system navigation, movie discovery, and todo list management into a single MCP endpoint, the server allows AI assistants to fetch real‑time information and manipulate data without leaving the conversation. This eliminates the need for developers to write custom connectors or API wrappers, saving time and reducing complexity in building AI‑enhanced applications.

The server solves the problem of contextual data access for conversational agents. In many AI workflows, a user may ask a question that requires up‑to‑date weather forecasts, file listings on a shared drive, or the latest movie releases. Traditionally, developers would need to integrate separate REST endpoints, handle authentication, and translate responses into a format the assistant can consume. My MCP server centralizes these operations behind a single, well‑defined protocol that the assistant can call using its built‑in tool invocation mechanism.

Key features of this MCP implementation include:

• Weather Service – Provides current conditions and short‑term forecasts for any location, enabling weather‑aware recommendations or alerts.
• Directory Listing – Exposes a safe, read‑only view of file system directories, allowing assistants to browse project files or user documents on demand.
• Movie Search – Integrates with a movie database to retrieve titles, ratings, and metadata based on user queries.
• Todo Management – Supports CRUD operations on a simple todo list, letting users create, update, and complete tasks directly through the assistant.

These capabilities are exposed as tools in MCP terminology, each with a concise prompt template that guides the assistant on how to phrase its request. The server’s sampling endpoint can also be leveraged for controlled text generation, making it useful for drafting emails or summarizing documents.

Typical use cases include:

• Developer Assistants – Quickly pull in project files or schedule tasks while coding.
• Customer Support Bots – Offer real‑time weather updates or product availability without hardcoding APIs.
• Entertainment Applications – Suggest movies based on user mood or preferences, with instant data retrieval.
• Productivity Suites – Let users manage tasks through natural language while the assistant handles persistence.

Integration is straightforward: an AI client authenticates with the MCP server, discovers the available tools via the endpoint, and invokes them by name. The server returns JSON responses that the assistant can incorporate into its replies. Because all services run on the same MCP instance, latency is minimized and security can be centrally managed.

In short, My MCP Project delivers a unified, extensible platform that bridges the gap between conversational AI and real‑world data sources. Its modular design encourages rapid prototyping, while its adherence to MCP standards ensures compatibility with any assistant that supports the protocol.