MCP Add Server

The MCP Add Server is a lightweight, Model Context Protocol (MCP) implementation that exposes a single arithmetic tool: . By adhering to the MCP specification, it allows any compliant AI assistant—such as ChatGPT, Claude, or custom bots—to discover and invoke this tool over a standard transport (e.g., stdio). This solves the common pain point of integrating external services into conversational agents: developers no longer need to write custom adapters for each tool; instead, they can publish a minimal MCP server that any client can consume.

At its core, the server listens for tool‑call requests, validates the input parameters, performs a straightforward addition, and returns the result wrapped in an MCP‑compatible response. This simple yet fully compliant design demonstrates how to expose domain logic through the protocol, making it a perfect reference for building more complex MCP services. The server is bundled on npm as , enabling rapid deployment via a single command () or global installation.

Key capabilities include:

• Protocol compliance: The server implements the MCP handshake, schema definition, and response formatting required by clients.
• Tool registration: It registers the tool with a clear JSON schema for its arguments ( and ) and the expected output.
• Transport agnostic: While it defaults to stdio, the architecture allows switching to other transports (e.g., TCP or HTTP) without changing client logic.
• Extensibility: Developers can fork the repository, add new tools, or modify the existing one while preserving MCP compatibility.

Real‑world use cases are abundant. In a data‑analysis workflow, an AI assistant can automatically compute intermediate sums or aggregate results from user‑supplied data without embedding the logic directly in the model. In educational tools, a chatbot can teach arithmetic by delegating calculations to this server, ensuring consistent results and simplifying the model’s reasoning process. For rapid prototyping, teams can expose any small utility (unit conversions, date calculations, etc.) as an MCP service and let the assistant orchestrate calls between them.

Integrating the Add Server into AI workflows is straightforward: once the server is running, any MCP‑enabled client can query its capabilities via or a local registry. The client then constructs a tool‑call payload, sends it to the server, and receives the computed sum. Because the protocol handles serialization, error reporting, and schema validation, developers can focus on building higher‑level conversational logic while the server reliably executes low‑level operations.