SimpleCalculator MCP Server

Overview

The SimpleCalculator MCP server provides a lightweight, container‑ready implementation of the Model Context Protocol that exposes basic arithmetic operations as first‑class tools. By encapsulating the calculation logic inside a Docker image, developers can deploy the service quickly and reliably across any environment that supports containers, ensuring consistent behavior from local machines to cloud orchestrators. The server’s purpose is to give AI assistants a dependable, low‑latency backend that can perform numeric computations on demand without relying on external APIs or complex dependencies.

At its core, the server implements four primitive mathematical tools—addition, subtraction, multiplication, and division—each defined as a separate MCP endpoint. When an AI client sends a request, the server parses the input, executes the chosen operation, and returns the result in a clear, human‑readable format. This straightforward contract makes it trivial for an assistant to incorporate the calculator into a broader workflow: for example, calculating budget totals, evaluating user‑supplied formulas, or validating numeric data before it is stored in a database.

Key features include:

• Protocol‑centric design – The service follows MCP conventions for tool discovery, request formatting, and response handling, allowing seamless integration with any client that understands MCP.
• Containerized deployment – A Dockerfile and compose configuration expose the server as a stateless microservice, simplifying scaling, CI/CD pipelines, and isolation.
• Python packaging best practices – Modern tooling (, ) ensures reproducible builds and minimal runtime overhead.
• Basic security – While the implementation relies on container isolation, it demonstrates how protocol boundaries can be enforced at the network level.

Typical use cases involve any scenario where an AI assistant needs to perform quick numeric calculations without external calls. For instance, a chatbot that helps users manage personal finances can use the calculator to compute monthly expenses or loan amortization. In data‑analysis pipelines, a model might invoke the service to validate intermediate results or generate summary statistics on-the-fly. Because the server exposes operations as discrete tools, developers can compose complex workflows by chaining multiple MCP calls, leveraging the same patterns used for more sophisticated external services.

The standout advantage of this MCP server is its simplicity coupled with full container support. Developers familiar with MCP can drop the image into their stack, expose it behind a service mesh or API gateway, and start building richer AI experiences that require deterministic arithmetic. The design also serves as a template for extending the protocol with additional mathematical functions or domain‑specific tools, illustrating how custom MCP servers can evolve alongside an application’s needs.