Simple MCP Server

The simple‑mcp project is a lightweight yet fully functional Model Context Protocol (MCP) server that demonstrates how an AI assistant can be extended with custom tools and data sources over a standard I/O transport. By bundling both client and server in one repository, it gives developers a ready‑to‑run example that showcases the core MCP workflow: a client sends a request, the server executes one of its exposed tools, and the response is streamed back in real time. This makes it an ideal starting point for anyone looking to prototype or embed MCP functionality into larger systems.

At its heart, the server exposes a single “hello” tool that simply greets a user by name. While minimal, this example illustrates the mechanics of registering tools, handling parameters, and returning structured results—all through a plain text pipe. The client side is equally instructive: it can connect to an already running server or spawn a new process, choose tools interactively, and even run queries directly from the command line in one‑shot mode. The robust stdio transport includes timeout handling and error reporting, ensuring that both sides stay in sync even under adverse conditions.

Developers will appreciate the server’s emphasis on reliability and observability. Comprehensive logging captures every request, response, and error, making debugging straightforward when integrating MCP into complex workflows. The ability to run the client in interactive mode allows rapid experimentation with tool parameters, while one‑shot mode is perfect for scripting or CI pipelines that need to trigger a single operation. The clear separation of concerns—client, server, and test harness—provides an excellent template for expanding the toolset or swapping in different transport mechanisms.

In real‑world scenarios, a simple MCP server like this can serve as the backbone for domain‑specific assistants. For example, a customer support chatbot could expose a “lookup ticket” tool that queries an internal database; a data‑science assistant might provide a “run analysis” tool that launches Jupyter notebooks. Because MCP decouples the assistant’s core logic from external services, developers can iterate on new capabilities without touching the AI model itself. The example’s modular structure encourages adding more sophisticated tools—such as API calls, file operations, or custom inference models—while preserving the same client interface.

What sets simple‑mcp apart is its educational clarity. Every component is deliberately straightforward, yet it adheres to production‑grade practices: proper error handling, timeout management, and detailed logging. This makes it an excellent reference for understanding how MCP servers operate under the hood, and it provides a solid foundation upon which to build more complex, feature‑rich assistants that can seamlessly interact with diverse data sources and tooling ecosystems.