Simple MCP Server

Overview

The Simple MCP Server is a lightweight, reference implementation that demonstrates how to expose custom tools and resources over the Model Context Protocol (MCP). It is designed for developers who want a quick, ready‑to‑run example of an MCP server that can be integrated with AI assistants such as Claude or OpenAI agents. By providing a clear, minimal code base and simple configuration steps, the server removes the friction of setting up an MCP service from scratch.

At its core, the server offers two distinct tool sets. The Weather example connects to the OpenWeatherMap API, exposing a method that returns current weather data for any city. The Note Keeper example implements a tiny persistence layer backed by a local Markdown file, offering , , and actions. These tools illustrate how MCP can expose both external API calls and local stateful operations, giving developers a concrete template for building more complex services.

For developers working with AI assistants, the server’s value lies in its ability to turn arbitrary logic into callable actions. An agent can query or modify external data without embedding the logic directly in its prompt, leading to cleaner, more maintainable code. The server also demonstrates how to run an MCP instance as a subprocess and interact with it from within another Python process, which is useful for building end‑to‑end agent pipelines that include local services.

Typical use cases include:

• Rapid prototyping of new tools for an AI assistant, such as integrating a weather API or a task‑management system.
• Educational demonstrations of MCP concepts, showing how tools are registered, listed, and invoked through a web interface.
• Testing agent behavior in controlled environments where the tool responses can be mocked or monitored via the local server.

Integration is straightforward: once the server is running (default port 6274), any MCP‑compliant client can discover its tools via the endpoint, connect using the provided Web UI, and invoke actions by name. The server’s design follows MCP best practices—environment variables for configuration, clear separation of tool logic, and a minimal dependency footprint—making it an ideal starting point for building production‑grade MCP services.